text:
How do I voice my opinion that the teaching of students is being neglected?
I'm in a Phd program right now and it seems there are some unethical practices going on in my department. Examples include: testing over irrelevant material or material that was not really covered, not grading/giving feedback on homework, instructors breaking college and department policy for convenience, dishonest course description and listed prerequisites.
When I got my masters in a different discipline at another institution, I felt everything was so well put together and my instructors really cared about the class and the students. I have considered leaving with a masters in stats since I feel slimy for participating as a TA in what I mentioned above, but I would like to reach out. Does anyone care? Would anyone listen? Who should I reach out to? How should I phrase my concerns?
I can include more details if that would help.

EDIT
I wanted to give more details:
I'm in a Phd stats program right now and I have a masters in math (I was all but dissertation)
I TA for a masters level core class. The instructor doesn't grade, lecture or make notes for the class (they are reusing the video-lectures and notes of another instructor from previous semesters). As far as I am aware, the instructor just pulls questions from a bank of questions for tests/homework. When I learned the material, I took a two semester sequence to cover everything and we still didn't cover all the material this class does. Every semester, masters students spend more than 1000 USD on the course and have to drop due to lack of background even though they work hard. Last semester about 25 out of 80 students dropped. The only prerequisite for the course is calc III.
For another course, the boss (instructor) of another TA in my cohort tried to make the TA create the assignments and tests which is against department policy.
In my case, I have had several instructors whose exams don't reflect the material covered in the homework and I feel that I could have not studied and done just as well (bad) because the exam questions end up being some difficult honors calc II question instead of using the material we cover in class. In fact, one instructor got stuck for a few minutes when giving us the solution to a problem that no one was able to solve on an exam. A subgroup of my cohort is unable to attend office hours for a required class due to having another required class and the instructor was unable to accommodate them due to "being too busy with research".
In summary, I feel that the department is trying to make money off of master students and is more focused on research than being good teachers.
Maybe this is normal and didn't know since my last institution was really good. Just let me know if it is. Thanks for your time.

PhD classes are usually somewhat "casual" and you may be expected to go well beyond what was covered in class (compared to a master's). So it's hard to say whether this is truly unethical or just a change in expectations based on your limited descriptions

One point: if you are a TA, then the instructor for your class is probably not your boss. If you get asked to do something which is against department policy, you might be able to get away with refusing, or even with ignoring the request. I'd recommend asking your graduate director and/or department chair.

I'm actually noticing this is mostly a duplicate for this question: https://academia.stackexchange.com/questions/86438/what-should-i-do-as-a-ta-if-the-instructor-is-not-teaching-properly?rq=1

The fact is that institutional norms and expectations regarding teaching vs research for faculty vary widely. At my current institution, teaching is very important. At my previous institution, no one really cared. Basically, I would not voice this opinion in any strong way unless you want to make your life difficult. It would only work if the faculty member you are working for is disliked and the person you complain to is firmly on your side.
I did have a similar situation as this in my PhD institution and "voiced my concerns" in a very tactful way. I argued that the course was extra challenging because it was a new advanced level course and had not been given enough resources. (Note that I was careful to say nothing about the effort or quality of the instructor.) The department was able to pay me extra as a TA to take over some of the instructor’s responsibilities. But the instructor was someone who got on the other faculty’s nerves, and my advisor was the associate dean for academics.

Thank you for your response, it was useful. I think I will remain silent for the time being and think about if I want to continue here.

Keep in mind that as a grad student you are not part of the permanent faculty. You are supposed to be there for a few years, get your degree, and leave. It is the responsibility of people like the Chair or Director of Graduate Studies to decide on these bigger issues. If it really bothers you, you could discuss with them, if you trust him/her. But if they don't seem interested in changing things, don't press them on it.
Don't create any drama, don't lecture the tenure-track/permanent faculty about how teaching should be done (or more broadly about how the department should be run), don't send passive-aggressive e-mails to the department, don't complain on Twitter or social media about what you believe is a neglect of teaching quality in your department, etc.
Remember to maintain a good reputation in the department. In the meantime, just observe and think about what kind of department you would like to be in if you want to continue in academia. Use that to inform your job search. If you get a position where you have more of a say in how the graduate or undergraduate program is run, you can bring up your experiences and ideas.
All of this applies if you are a postdoc/Visiting Assistant Professor.

Apart from the bit about not complaining on social media, I strongly disagree with the 'keep your head down' mentality. It should be a matter of going through the correct channels instead of being silent for professional expedience. Every University I have been in has an academic assigned to oversee teaching for each department, and they take their position fairly seriously in my experience. If this is the case in OP's university, I think that voicing their concerns to the responsible academic may help.

The way OP has phrased the question, it seems like the problem goes beyond a matter of disagreement on teaching style and instead borderline negligent teaching. Keeping silent about such things just normalizes poor teaching practices and speaking  (to the correct people) is fully warranted IMO.

That's reasonable. To expand on my response, there may be appropriate channels for OP to express his/her concerns - I had in mind the Graduate Chair, but there may be other people that I'm not aware of. However, if the Graduate Chair or Chair believes that things are fine as they are, then I wouldn't push any further.

Also, it's not just a matter of advancing oneself professionally. When grad students/postdocs are hired, they are not hired to inform the department about how it should be run. They should bring up issues of concern to the right people, so I hope OP does talk to someone. But I have seen enough drama created by supposedly well-intentioned grad students to know that, when done in a divisive way, it annoys everyone in the department, and not in a productive way.

I would argue that graduate student campaigns can be long-lived and effective, though the individuals may rotate through. Graduate students have a valuable perspective that can help the department stay more true to its goals: they are likely more diverse and better reflect the diversity of the undergrad population, they care about the institution and want to see it uphold its promises without being jaded, and they are more invested in the future of the field. Faculty management has less accountability and can get pretty ineffective and stale without grad student input.

"be there for a few years" should mean actively participate in academic community life. Remember that it is mostly the "temps" who do TA work. -1 for discouraging OP from actively participating and suggesting that they keep their reservations out of the public eye.

Agreed on all the answers above about the incentive for not making waves, and that any effective approach will create more work for you. It's probably in your best interest professionally to ignore these problems, do what you can to help the students you have access to, and spread information invisibly about highly dysfunctional people/classes to avoid, especially if your goals for your career path have strong research components (ie, you want to be an R1 professor or work at a national lab).
If you DO decide to take action, however, here are some moves you can make to put you in a position to affect positive change:

Look for department committees to join. There will likely be a curriculum committee, and they may have a spot for a graduate student to join. If they don't have such a spot, get in touch with the faculty on the curriculum committee and express interest in how the committee works/what it's like to be involved in that process. If you can't get on a curriculum committee, getting on some other committee could still put you in a place where you can affect change (for example, a soft committee like graduate student life or something could still put you in the right place at the right time)
Look around for student groups that are actively pursuing campaigns to change things (they will likely have a more euphemistic goals like "improve life" etc). Focus on groups nearby your department and expand outward as necessary. If there's a group like "EDI in math" and your department is math, start there. Sit in on a meeting or two to see if there is already an ongoing campaign to advocate for higher accountability when it comes to teaching. If no such campaign exists, voice your concerns about what's going on. If what you've noticed is a big problem negatively affecting a lot of students, you'll see that right away from the reaction of the group. If the group isn't quite the right setting, usually the people involved in these things are otherwise tapped in to organizing and can point you in the right direction.
Go to department town halls or other events where faculty directly ask input from graduate students in your department. Prepare what you're going to say a bit (you don't want to come of as complaining or nit-picky, but you do want to come off has having a few ideas that are easy to implement that would make a big difference). Voice your concerns about teaching, present your strategy for improvement. Get ready to be interrupted/talked over/shut down with off topic random opines from faculty, etc etc. This is hard but if you play it right you can germinate your ideas in a few faculty's minds. I brought up a problem in this setting, and got the distinct impression the faculty didn't really hear what I was saying in the moment. One member of the faculty, however, did follow up with me afterwards, and I actually got the wording changed on a handout given to students (small achievement, I know).
Literally take things into your own hands and start an activity that will create a better support system for students. Examples: create a website that compiles practice problems for a course that is frequently poorly taught and doesn't give enough practical examples, create drop-in tutoring sessions to give students more access to people who know what they're talking about and can actually help (and helps students get to know each other and work together to teach themselves).

I've seen these strategies actually make a difference in some departments, but I'm not going to lie: progress is very slow and very hard. The examples I provided in the last bullet point (website + tutoring center) I actually saw implemented in my PhD department, completely driven by basically volunteer work from graduate students. I think the best attitude is to accept that teaching quality is low and the resources individual professors are providing students now won't change. Instead, think about how you can give students more resources by utilizing the institution: set an example starting with your own volunteer work and hope the department will like it enough to keep the activity going after you're gone.
Also a lot of these courses of action are going to give you a reputation as someone who cares a lot about teaching, which may or may not be a good thing for you depending on your career goals.

Agreeing with people before me, I would say that you cannot change a lot. You have to understand that what you are observing, with number of students you are referring to, is not secret. People whose job is to change that (if they feel to) probably know about it and decided to keep it that way. So you have to assume that the institution is fine with that course, as it is handled now.
So your job if you decided to change how the course is executed would not just be "letting them know" or "opening their eyes". It would be convincing them that the course needs changes, even though the people think it does not, and base this opinion on fully knowing what it is going on. And this ... is a very very uphill battle and I am not sure you are in the right position to do it.
Now, I may speculate, why this is as it is. Maybe the institution prides on itself to be "extra selective" where the selection criteria is ability of students to do the work themselves. Or maybe they don't care. 1000 USD seems to me a low price for a course, so maybe the institution feels that the what the students get reflects the price.


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How good do master's rec letters have to be?
I'm going to be out of undergrad working for 2-3 years before I go back to grad school. My end goal is a PhD, but I would do a Master's first because I think I would be able to hack it financially and my research experience is sort of weak. My only concern is being out of school for that long and needing to go back and get letters. Which brings me to my question: do letters for MS programs have to be as good and detailed as those for PhD programs, or is it O.K. for them to be positive but generic if everything else in my file looks good? Assume I want to get into some >40 rank programs. I'm in the US and my field of study is (the neighborhood of) CS/Applied Math.

This is heavily dependent on what country you want to study in. Probably field of study also.

Good point, I'll add that info.

You should probably register, also, to make communication easier here.

Does ">40 rank programs" mean "better than or equal to #40" i.e. top-40, or does it mean "rank # is more than 40" i.e. 41st and below?

Unless you want an MS for its own sake, in the US you should probably just apply to a few doctoral programs. Most applicants here hold only a BS/BA degree. Few programs require significant research in that case. It also gives you the opportunity to earn a stipend as a TA to support your studies, including paying for your tuition.
One problem with taking a break and then getting an MS is that you still need to pass comprehensive exams in most programs. The MS may prepare you well for that or not. Starting a doctoral program after a BS leads you through the required advanced courses to enable that. In many such programs you can earn a masters along the way for little effort.
In the US, letters of recommendation are quite important as they give information beyond the formal record, including making it possible for an admissions committee to make a fairly accurate prediction of a candidates likelihood of success. Without good letters, admission is difficult for any graduate degree program, but especially if it is research focused.
The implication is that you should stay in contact with your professors if you need to take a break. Visiting once a year to "catch up" with professors who have worked with you is a good plan. Keep them up to date on what you've been doing and your goal evolution.
Some of the "top 40" programs are State Universities with relatively large programs. Don't restrict your search too narrowly. There is a lot of competition and any of the R1 universities will likely do a good job.

That is an interesting point about the comprehensives, I hadn't thought of that. Not to mention that it will take longer to do the MS first and then the PhD. I'll have to seriously think about whether all that's worth the tradeoff to get the MS first and use whatever research experience I gain there to try for a "higher prestige" PhD. Thanks!

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Is there a difference between simplification and spoon feeding in education?
I had been researching about what students dislike about studying, and found out two contradictory piece of reasons:

students cannot understand the concepts taught to them by the teachers;
they hate spoon feeding.

Most articles on the internet (I think almost all of them) talk about how spoon feeding is harmful in education. But if 1 were the reason why students dislike learning, isn't it an imperative to make acquiring knowledge easier? I know lots of sites on the internet which simplify and explain topics carefully. Is that a form of spoon feeding? Or is that of simplification? Are there differences between them?
Should spoon feeding be stopped? Or should sources be more simplified?

Keep in mind that the collection of students who don't understand the concepts and the collection of students who don't like spoon feeding might have a very small intersection. Complaints, especially on the internet, usually arise from those at the extremes, not the middle.

What is your definition of "spoon feeding"?

And, what level of education are you speaking/researching about?

"[I]sn't it an imperative to make acquiring knowledge easier?" Brain-chip implants? I suspect it's simply that some people who can't swim find themselves unexpectedly in the deep end of the pool.

I think that these concepts don't really overlap that much:
Simplification is about how you explain something. When you explain a concept, you might leave out some details which make the concept more complicated to grasp. By focusing on the most essential parts (and possibly adding the details later), you don't overwhelm the students with information and allow them to understand a concept in a "step by step" way.
Spoon-feeding is about how you let students solve tasks, in classroom or homework assignments. The goal of such assignments is to let students practice the learned concepts by applying them in an independent way. Spoon-feeding means that the students don't need to think because you have already done the thinking for them and just let them execute some clearly defined, straightforward tasks.

+1. But I would like to add that while "simplification" can be beneficial to some students, but other students may think that simplification hampers their understanding because "things do not make sense" if too many details are left out.

@Louic Indeed! It never hurts to say something like "That's the basic idea. But there are actually more aspects to it, which we will touch on later / you can read about in ..."

re: @Louic's point, a specific case where I've seen this happen a lot is in omitting math. There are some things, like in basic physics, that are really straightforward if you use math, and really complicated otherwise. Sometimes people try to make physics "simple" by leaving out math since some people struggle with math. The result is a curriculum of memorizing nonsense rules because if you don't allow yourself to touch an equation then memorization is all you can really do.

There are important differences between simplifying and spoon feeding:

simplifying: leaving details out that are not important for the moment. For example: not mentioning exceptions to certain rules.

spoon feeding: sharing knowledge slowly, small bits at a time, and not teaching students to think for themselves or understand the underlying concepts. A typical example of spoon-feeding is telling students what they need to memorize to pass the exam, but not teaching the (useful) underlying concepts that allow them to judge new situations (even though these new situations may not be part of the exam).

Of course we cannot tell if these interpretations are exactly what the students in your question mean. It would be best to ask them to clarify and provide examples.

It really depends on the topic. I guess with every class you need to find the balance, and it's ok to ask your students in the beginning (or throughout) what they need explained and what is clear.
If you cannot do that, e.g. online teaching makes it a bit harder to read their expressions and often also means that fewer people respond, make sure to explain all concepts that are needed for the basic understanding of the central topics. To not sound like you think they know nothing, you can try and say something along the lines of "as you probably know/may have learned in high school ... "
I real-life classes I find that engaging with the students is the best way of gauging how much they get and where their insecurities may lie. You can do that in conversation much better than by talking down to them. It feels like you're guiding them through a new topic using their knowledge and building new knowledge. This may also lead to better results because their knowledge is expanding in a network.
I feel like spoon feeding is different and students hatred of it revolves around something else. Based on my own undergrad experience, there were a few lecturers who would just give us factoid after factoid and explanation after explanation but I never felt like I got an understanding of the topic as a whole. Better lecturers, on the other hand, would give you a feel for the whole topic and individual classes would call back to each other. We would build upon previously discussed topics and they would link up in complex ways. So, as we progressed, our way of thinking about the topic also had to become more complex and less straight-forward.
This is hard to explain and a very large topic. I hope, some of theses thoughts help.

Simplifying is not all or nothing, but you can simplify to some degree. If the students don't get what you want to teach them, then you haven't simplified enough. If it is too easy, then you have simplified too much. So it is all about finding the right balance for your students. Keep in mind that there is quite a bit of variation within students, so that complicates that equation even more.

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Etiquette: How many reminders should one send for conference speakers to submit their talk info?
I am co-organizing a conference that will take place virtually on Mar 13, 2023. We invited the speakers and they confirmed their participation in Nov, 2022. Now, we sent them a form that collects their talk info + bios + photos, and a consent form to sign in Jan. and some of them have not submitted their info yet nor emailed us the signed consent form until now.
We sent a reminder on Feb 2 and they have not submitted the data yet! - I know that professors are busy but also this is putting a lot of stress on us, "the organizers", the registration for the event ends on Mar 9!
It is a volunteer conference so we offer no honorarium to the speakers and maybe that is why they are not taking it seriously but we (the organizers) are taking our volunteering work seriously and we care about the details!
I am uncomfortable asking them again to submit their info. What advice do you have for me?
Those who are late on submission are old professors and probably busy, and I am a graduate student.
And also, how to write an effective but gentle reminder email to the professors who are late on submission?
Here is what I came up with:

Hello all,
Hope this email finds you well. We are very excited that (conference
name) is approaching and we cannot wait to meet you all!
Could you please fill out these forms and email us the consent form if
you have not done so at your earliest convenience ideally before the
end of Feb? - Registration closes Mar 9.
Thank you.
BR, xxx

You didn't really give a deadline.

I am trying to tell them that the deadline is Feb 28 in a polite way. I am uncomfortable telling them the deadline is Feb 28.

"Ideally" is just a suggestion, not a deadline. Be Bold.

What is the relation between them providing this info and registration closing?

Why do they need to consent to talking?

I bet it is a photo release or similar!

@TimRias So that people registering can decide if they're interested

@AzorAhai-him- For most people, simply the list of invited speakers should be enough to do that.

"Hello all,
Hope this email finds you well. We are very excited that (conference name) is approaching and we cannot wait to meet you all!" Consider that such impersonal, generic headers are awfully close to the conference spam a researcher will be getting regularly. Having an attachment (for the forms) could strike even more red flags for spam.

As someone who took part in organising an international conference as an undergrad student and was responsible for sending out e-mails to participants and speakers, I can report that not a single recipient took any kind of umbrage to receiving ‘strong’ messages from a mere student. Most presumably didn’t know I was just a student – they just saw an e-mail from a conference organiser. Repeated reminders of specific deadlines (polite, of course) were appreciated across the board.

Being polite and being firm aren't mutually exclusive.  Change your text to "Please fill out and email us the consent form, if you have not done so already, no later than 28 Feb.  Otherwise, we will assume that you are not able to serve in a speaking capacity, and look forward to your registration as an attendee no later than 9 Mar.  Thank you for helping us make this a great conference, and we look forward to hearing from you."

As many reminders as it takes to get the information! I would keep a few things in mind. Decide on a firm deadline. Put it in the subject line. “Deadline 2/28: Submit program info for Name of Conference” Then send this to each person by name. When the email goes to many people (“Dear All”), it seems that many people are late. If I get a personal note, I get the impression that everyone else has submitted. Good luck.

I put something like [Action required] at the start of the subject. And +1 for personal emails, not 'dear all'.

If it’s an international conference, I’d avoid writing “Deadline 2/28” – non-Americans are likely to read that as ‘deadline 2 of 28’. Better to avoid ambiguity and write, “Deadline 28 Feb[ruary]” (or “28th Feb[ruary]” or whichever style of writing it out you prefer).

Send one more and give a deadline (firm). "If we don't have your material by -firm date- you won't appear in the program."
If the recipients don't see the consequences of being later then there is little incentive (from their standpoint) to be "ideal". Make it clear.

I agree with providing a firm deadline, but the wording ("you won't appear in the program") seems a bit too strong (even if true), given that the speakers are volunteering their time and effort.

@GoodDeeds, how about "can't appear"? The volunteers can't just make stuff up.

The way busy people read email is often a triage system. People make a quick decision on whether the email requires a response immediately, requires a response later, or doesn't require a response. For busy people with a lot of emails, the "requires a response later" category can quickly slide into "never going to get around to it."
So your strategy should be to make it extremely clear that your email does require a response immediately. Don't worry about being overly polite -- often including additional extraneous words beyond your request simply makes it harder for the person to determine what you need, which makes them more likely to decide that the email is not something they need to respond to right away. Just state what you need simply firmly and make it clear there is time pressure. Furthermore, personalizing emails adds pressure to respond.

Dear Prof X,
We are excited for your upcoming talk at the Y conference.
The deadline for title/abstract/bio submission is Z. Please make sure to submit your material by the deadline so that we can include your information on the website.
Thank you,
Nadine

+1  "often including additional extraneous words beyond your request simply makes it harder for the person to determine what you need,"

It seems you think it is rude to tell them about a deadline. What is ten times ruder is not telling them about the deadline, they miss it, and as a result can't speak on the conference.
So tell them. Using words that are hard to ignore.

I would suggest being more direct. Explain it as your problem (like the printer of the program being a stickler for a deadline). But make very clear what you need doing and by when. Otherwise many will not even realise the need. If you want additional background put that after the call to action.


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I have found a mistake on someone's code published online: what is the protocol?
I have been using an econometrics toolbox for around 2 years and it is great, very useful. It is available from someone's personal website.
I have found a mistake in one piece of code which has set me back for a considerable amount of time. It is a small change but with vast consequences. Instead of questioning the code, I did my idea.
Now that I have found the problem, what is the protocol for highlighting this? Is emailing the author necessary?

I have edited this to include information from your comments to the answers below. Please edit further if more clarification is necessary or if my edits are inaccurate.

Is it available on github/bitbucket? If so, just make an issue or even better fork and submit at pull request.

What is this mistake? I mean: the issue is that the code does not do what the author thinks it does (implementation error) or is the author logic simply wrong for the task they want to solve (semantic error)? In the first case it's generally not a big deal, open an issue in the repository if available or send an email to the mailing list if it exists or just to the author. If it is the second it may be worth a small publication depending on the exact circumstance (if the tool is extensively used by others, whether your solution is a novel idea etc).

Although it can be rude to point out others' mistakes in general, when it comes to code, it is much appreciated.

@Evorlor How can pointing out mistakes be rude? That's how we learn.

@user2768 It can be phrased in a rude way, or same people take offence even if it is phrased nicely.

sub-question: should I inform them anonymously

@gerrit It can be phrased in a rude way, but that seems orthogonal. Also, I agree, some people take offence.

@user30609 Is there any reason to do so anonymously? It seems like it would hinder the process for no advantage.

@user30609 in case they are offended

@user30609 I don't think you'll cause offence.

Obligatory xkcd: https://xkcd.com/386/

Write a test demonstrating the bug.  Write a fix that makes the bug go away, as indicated by the test passing.  Submit that.

Now that I have found the problem what is the protocol for highlighting this, is emailing the author necessary?

It isn't necessary, but it is the right thing to do, if you don't, you're responsible for causing others set backs that will waste them a considerable amount of time.

fair comment, it has been annoying to me, I will think of a nice way to write an email,

@user30609 Don't overthink this, the email just needs to say something along the lines of: Dear A, I have been using your econometrics toolbox for around 2 years and it has been very useful for my work. <<Optionally mention some of your work, possibly wrapped in parenthesis.>> Recently I discovered a small mistake in the code: <<describe mistake (and fix if known)>>. Perhaps you can implement this fix and include it in your toolbox; it would save others the frustration of debugging for hours.

Coders love free fixes to their code.  The two times I notified the coders and supplied the fix along with the e-mail, it got deployed within 24 hours.

@Nelson Coders ... probably. But let's be be honest: There's lots of PANTA code in academics (Publish And Never Touch Again). One could play devil's advocate and say: If they were interested in maintaining the code, then they'd put it on a public SCM, and not only offer some ZIP on a personal website. But it's worth a try, of course: The worst thing that can happen is that the message is ignored...

@Marco13 It could be that they simply don't know about source control. Until very recently, even fresh CS grads seldom knew about it.

@user2768 Don't call it a mistake; that's rude. I recently overcame a hurdle in my project by modifying XYZ in your library. I am hoping that you can take a look and determine if this change needs to be applied to your source code or if I've hacked your code in an unsupported way, thank you.

@MonkeyZeus I've never got this. Why is it rude to point out a mistake, when it's a mistake? Everybody makes mistakes. It's clearer to say it's a mistake that needs fixing, instead of "overcame a hurdle" which would take me a while to parse. Isn't wasting time ruder than bluntness?

"if you don't, you're responsible for causing others set backs" -- hard agree. Remember you found that one oddity when using OSS and you never reported it? That oddity hurt my productivity, as a result I no longer have my job, and I'm homeless -- thank you a lot for ruining my life, pal. Oh, you did report it? Uhh... where? Why would you report it on some forum that I've never heard of in my life? Are you withholding some information from me specifically? I'm gonna learn ya to take responsibility for your actions. My lawyer will be in contact soon.

@vaultah That's parabolic. When you're using OSS, you have to not only anticipate, but to assume that people will mess things up (and the usual disclaimers can be found in the LICENSE.md). The ideal benefits of OSS come into play when everybody plays the game according to the rules, and many academics don't. When the funding is over, they don't give a  .... care about the code, and projecting the responsibilities on others is inappropriate.

Agreed. The principle is elucidated in Matthew 18:15-17, which can be VERY loosely translated as, "If your fellow programmer errs, go and show him the mistake, one-on-one. If he fixes the bug, he'll probably be grateful to you, and you'll have made a friend. If he doesn't fix his bug, then ask one or two others who might have more success persuading him to give it a try. If he still won't fix his bug, them go public, and publish the fix on-line."
It's really just an application of the Golden Rule.

Of course the OP is not liable for any resulting damages if he does not report this. I think this answer does not mean "responsible" in a "you are to blame" sense, but more "you could have prevented it with minimal effort" - and many people sleep better at night and can lead a happier life, if they know they did the right thing, independent of having to do it or someone else being at fault.

@Falco No, by you're responsible for causing others set backs, I really did mean, you're to blame for causing others set backs. (There's no issue of liability for any wasted time.) I believe the OP has a responsibility to act.

@user2768 I don't think I can agree with this. I would not blame someone for not going out of their way to possibly help strangers, where he doesn't even know if they want or need help and if the author actually cares. I think it is a nice thing to do and would also do it myself, but I would never blame someone for not doing it - I don't see it as a duty which needs to be done, but rather a good act which one might do if he wants to do something nice.

@Falco I see it as a duty. (It's worth noting that the OP is not going out of their way to possibly help strangers, they merely need to send a single email.)

@user2768 we're delving into morals/philosophical here. But there are probably a million little things (as small as sending an email) you could do every day, which would make other peoples lives better. If all of them were a duty, you'd have no time at all for yourself.

@Falco I'm merely identifying what I consider to be a researcher's duty. I don't consider the million little things to which you elude as part of a researcher's duty. You could write you own answer, given that you disagree.

I upvoted your answer, because you just write "it is the right thing to do" which is also my opinion. Only in the comments do you go explicit that you see it as a duty which is compelling to every researcher. The difference is just, I agree one is a better human if one does this, but not a worse if one does not.

I wouldn't be surprised if there are papers out there deliberately filled with errors with the explicit intention of wasting researchers' time, particularly the time of those researchers effective and conscientious enough to notice and take the time to point out these errors.

An important point of etiquette that has been skipped in the other answers:
Treat it as a suspected bug, and do not assume "I'm right, you're wrong" while presenting your fix.

No matter how confident you are on the matter, there is always room for having misinterpreted or overlooked something important.

Consider the case of finding an 'error' in code of:
(A + B)

And you decide that all of your use cases need this to be:
ABS(A + B)

Your use cases may not include a need for that possible negative to exist, or even be able to handle it when it does, but that does not mean cases outside of your consideration may not require them.

Start off with the assumption that the original coders know more about the code than you do. [They might not actually, but opinions of others can always be re-evaluated...]
Approach it as having a goal of mutually gaining a better understanding of what the code is doing, and what it should be doing, and how it is actually being used.
Consider phrasing any suggestions for changes/improvements as questions rather than commands. ["Do you see any flaws in my attempt at fixing my problem?" vs "You should use my code instead."]

Totally agree!!

The OP writes, I have found a mistake in one piece of code, so this doesn't really answer the question, it questions the legitimacy of the question and answers a different one.

@user2768 - They Think they have found a mistake, and they probably have, and they asked for a protocol on how best to handle it. Part of the proper protocol is to leave the door wide open to the possibility that you are in fact wrong about the nature of the mistake you've found... If you think this answers 'a different question', then you've entirely missed the point of the answer.

@TheLuckless You think the OP thinks, but they might really know there's a mistake. This isn't impossible, I've done it myself on numerous occasions. I've also thought there wasn't a mistake and I follow your advice when I'm in doubt.

@user2768 no matter how much one 'really knows' about a software bug, proper etiquette is to still assume you might be wrong, no matter how sure you are that you're right. It is the polite standing to start out such a conversation on, and covers your backside if you do actually overlook something. - Also don't forget that it is also entirely possible for BOTH parties to be wrong. Bugs in code and design aren't exactly uncommon...

I don't think it is appropriate to worry about who is right and who is wrong.  Maybe they are both right.  Maybe they are both wrong.  It does not matter.

@TheLuckless There are cases when I think that's just wrong, e.g., the first three digits of pi being defined as  3.13. In such cases, there's really no point in assuming you might be wrong, clearly approximating pi as 3.13 is wrong.

@emory: Humans are not robots. We are far more willing to listen to "Can you help me understand X?" instead of "X is why you are wrong." It's a matter of tact, nothing more.

@Kevin Suppose I write some software based on Freedom Units.  I can acknowledge that the software would be more generally useful if written in Metric Units without agreeing to a change.  I only care about my utility.  This is the beauty of open source software.  If you want, you can fork it to Metric Units without my permission.

You have 3 possibilities:

Contact the author as per the other answer. Best option based on the subsequent comments revealing that the source was a personal website.

If the code was published in an article or paper then the following may be applicable:

Contact the editor.
Publish a paper showing your work to improve the usefulness of the code.

The third depends on the type of error and how it was corrected - if it meant changing a 3 to 5 then that is trivial, but if it meant re-coding a significant portion with an extra process then it may well be a suitable option.
You have to tell somehow though.

I'll just add that if the code is on some online repository (e.g. GitHub) then there's a standard way of doing this.

It was a small change with vast consequences, I don't think it would warrant a paper, nor would I like to profit off of somebodies misfortunes.

It is on a personal website

Then it is clear you have made your decision - just say that it was so useful for you and you found the error...

(2) doesn't seem relevant from the original question, nor the OP's comment. (3) doesn't seem appropriate without further information: Writing a paper on a software bug seems rather unusual. I suppose (2) is relevant if the software's code were published in an article, since the editor may publish a note on the bug. Similarly, I suppose (3) is relevant if the software's code were published in an article. Perhaps @SolarMike had different scenarios in mind.

@user2768 I had assumed (incorrectly as it turns out) the the OP had got the code from a published paper of some sort. Now it has become clear that it was copied from a personal website that changes the game. Oh, if only all questions were written perfectly so that answers could focus on the correct direction...

@SolarMike assumption is the mother of all **** up's

@user30609 writing a clear question is useful on here, see https://academia.stackexchange.com/help/how-to-ask

Sorry @SolarMike I meant on my behalf, I should have written the question clearly and not assumed that the potential readers knew what I was saying

If you are the programmer yourself, and this is the open source project, best would be just to submit the pull request if they have the SCM repository. Both software would be fixed and you would get credits.
If not, report the bug, best over they bug database if they have any. Blogging about the issue on some random place makes little sense because it may take forever for the authors to find your comments.
If this is some obsolete team that has no repository and no bug database, may make sense to take over the project.

Many repositories have a file "CONTRIBUTING.md". The file contains guidelines for providing feedback, bug reports or how you open pull requests. (Example 1 , example 2)
Other projects have a web page or wiki with a "How to Contribute" section, where you can find this information.
If you don't find any of this, you should write an email with the bug report and ask the repository owner on the following procedure.

I'd just like to point that people are publishing code exactly because they want/expect some bugfixes/improvements. I've sent some fixes in my life (or just reported issues), and they were mostly well received. Even when I was wrong (turned out that compilers have the freedom to interpret "undefined behaviour" in the most liberal way).
So don't be afraid to send a report/patch!


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Why do some papers have 10-20 co-authors?
I am new to the field of academia, and when performing a literature review for really any topic, I am puzzled as to why some papers have so many co-authors.
Most papers have only about 1-5 authors which I can understand but I can't seem to understand how 10-20 people could all meaningfully contribute to a single paper. How possible is it that co-authorship is being gifted?
Are there any reasons why so many co-authors could be justified?
Update 11/11/19:
Some comments have suggested this question may be a duplicate. To clarify: I was not referring to large-scale taskforces that produce world-changing results such as the Higgs Boson project (What is the point of listing 1000 authors for a single scientific paper?). Those projects clearly play by different rules. I was meaning routine contributions to journals (such as a new formula or algorithm) where I struggle to understand how so many people could all meaningfully contribute to a small (albeit important) idea.

Strongly related and possibly duplicate Why do papers published by people in tech industry often have such a large number of authors?

What field are you talking about?

In mathematics, polymath projects are quite successful and have many authors.  See https://en.wikipedia.org/wiki/Polymath_Project

Seems to have been answered here: https://academia.stackexchange.com/questions/63440/what-is-the-point-of-listing-1000-authors-for-a-single-scientific-paper

The real reason is so that you can win an Ig Nobel Prize in Literature.

I'm on some papers with more than >500 contributers. All these belong to a collaboration of a particle physics experiment. Designing the machine, creating the machine, setting up the machine, analzying and predicting data, formulating mathematical and physical models. This single experiment started in the 90s and is currently build up. And the amount of researches grow over time, for sure. Authors of papers are all who are part of the collaboration when the paper is submitted and when the machine is running and gathering data, all former researches for the first three years.

I don't think this question is a duplicate. As explained in my answer, there are distinct cases of 10-20 author papers which don't apply to 1000 author papers and that do not include industry authors.

Also interesting to see how the trend changed over time https://www.nature.com/articles/d41586-019-03305-w

@Ben thogh I always thought that this habit in particle physics is a bit like adding Newton and Leibniz as a co-author whenever you work with derivatives

Some comments have suggested this question may be duplicate. While the other cited question has the same theme, the context is different. My question was not referring to large-scale taskforces such as Higgs Boson that attract 1000s of co-authors. I was referring to smaller routine contributions and as such attracted new, very valuable answers to the conversation not featured in the other question. These include: review papers, student projects, brainstorming projects, multi-disciplinary research, citing the data collector, multiple supervisors etc.

@HagenvonEitzen Though it's more sarcastic I will answer seriously: When all the people who contribute in designing, building and testing such a complex machine will never appear on a paper, this would mean they will never have any chance as a researcher. This, in return, will result in a huge loss of scientists and engineers which are necessary to proceed.
But to make you happy: I know no one who lists such publications in his CVs ;) I'm also on some papers which I never read.. and I don't care. But I also do not care about papers. Un-necessary cloudy convoluts of words..

@Ben Interesting point which leads to another question: What is the criteria for authorship? As Hagen says, should Newton and Leibniz be included as co-authors? Isn't it more appropriate to provide a citation to their key papers? As for, say, engineers that design equipment in the Higgs Boson, am I wrong in thinking they shouldn't be included as authors in physics papers; however, they should write their own papers specifically focussed on the building process which can be cited by the physics researchers?

Good question.. how to draw the boundary is probably quite broad. You're right in regards to the engineers, they probably do that. But, e.g., when the Higgs was discovered and the Higgs et. al. received the Noble prize, there was a great discomfort in the community as all the experimental physicists and engineers were not contributed. And the effort to "just prove this great theory" was incredible. But obviously not regarded as important (enough)..

In my field, where author numbers between one and six are the norm, reasons for having more include the following:

The paper results from a large-scale collaboration between different groups that developed different parts of the overall work.
The paper is supposed to give a broad picture overview of something, like in the case of a survey paper or a research roadmap paper. The authors are experts for the different considered areas.
The paper results from a collaborative brainstorming effort. Such papers are sometimes written by the participants of a research meeting (like the famous Dagstuhl seminars), or a breakout group from such.
The paper results from a student project, and most of the co-authors are actually students.

This is all accurate, but I think the answer would be improved by fleshing out the "large-scale collaboration". Some experimental work, especially in physics, can involve many people that do specialized work as part of the experiment which are acknowledged as authors for those specialized contributions  even if they did not aid in the drafting of the written document.

I'm right now involved in writing a manuscript about some experiments where many labs participated, and we had multiple meetings where all labs together decided/developed the details for the experimental procedure, so large-scale together with collaborative brainstorming.

De facto, co-authorship means different things in different fields.
As Zenon mention in their answer, the Higgs Boson paper has 5154 authors.  Does that mean that there is a first author who sent around the manuscript to all co-authors, then waited for 5153 people to give feedback and OK?  No, absence of response does not hold up publication and is taken as agreement on the content.  As I understand it, on this collaboration, the criterion for co-authorship were that you'd been part of the team for a year (source: personal communication).
In my field (Earth Observation), there are sometimes papers on validations or campaigns that include many different instruments.  For each instrument there is a PI and their postdoc and/or PhD student, who should be on the paper even if their only contribution is "provide data".  They need that to prove to their funding agencies that the data are being used for science.  With 10 instruments, that can easily mean 20–30 or more co-authors.
So to answer your question: it depends.  The reasons for papers to have many co-authors are field and even sub-field dependent.
See also: Academia varies more than you think it does – The Movie

"they need it for their funding" sounds as if they didn't deserve to be on the paper but get there because of merciful main authors and financial reasons. Contributing data is a sufficient reason on its own, you don't need to write text to contribute to a paper. Some PIs only provide ideas and proof-read their students, they still contribute, same here.

@FrankHopkins I meant, they need the co-authorship to prove to their funding agencies that their data are being used.  Will edit to clarify.

The papers in large particle physics collaborations are circulated to the whole collaboration. But this happens in the form of a bulk email telling where to look for the draft, where to send comments, and the deadline for comments. Lack of response for any given author doesn’t hold up the process. At least one paper I’m on went through four rounds of this and a in-person collaboration meeting.

same procedure (circulating to everyone with deadline for comments/changes etc.) for manuscript with >20 coauthors I'm involved with right now (analytical/physical chemistry).

There are many reasons. Science is becoming more and more collaborative, especially in fields requiring experiments. See for example the ATLAS and CMS paper on the discovery of the Higgs Boson which has 5,154 authors, or this 1000 Genome project paper which has hundreds.
Those are extreme examples, but with the increase in complexity it is often required to recruit collaborators with a large amount of complementary skills to be able to gather the data and analyze it.
There are also discussions about what really warrants authorship and how those rules are not necessarily clear. There is an interesting discussion on the blog of PLOS about that.

As others have written, this is extremely field dependent.
I work in two fields. In one (forecasting) the maximum number of coauthors has been five, including me, and that was a review paper in which everyone indeed contributed different expertise and a different view on a subfield.
In clinical psychology, I have collaborated on papers with up to ten coauthors, including me. To a certain extent, the number of coauthors was driven by different kinds of expertise. I did the statistics, other people did the fMRI analyses, yet other people the blood analyses, and the PI had the overall vision that got everything started, funded and seen through to the end. That's four people right there. Add a postdoc that supervised the day-to-day running of the project, and finally multiple people who did the psychological intervention, or psychotherapy. This is a very time-consuming effort, and you need multiple therapists to be able to process a sufficiently large number of participants, each of whom may require many one-on-one sessions. Plus, of course, all the ancillary activities like report writing for each and every participant.
Thus, one driver especially in clinical psychology is the sheer amount of work necessary to acquire each data point.

My maximum is 9 (here). It occurred on an interdisciplinary paper in Molecular Biology and Evolution.
Six from computer science:

The lead author was a student in our lab.  There's another student who helped him, who became coauthor.
Aside from me, there are three other teachers from our lab, who probably offered advice to the students during weekly research meetings.  I would guess one teacher came up with the basic ideas, and another teacher is the student's supervisor.
I surely would have done much of the paper revising.

Three from biology:

The professor whose biological data we use.
Two of his students, who I've never met and do not know their role in the paper.  I assume they helped the biology professor obtain the data.

Combining  methodologies often requires a team for each, with their own PI and then a coordinating PI.
For example a paper I'm familiar with looked at fMRI and gait metrics, so there was (1) 3-4 people who worked on the fMRI data, (2) 3-4 people who worked on the gait data, (3) a large group of senior professors who wrote the original grant and coordinated the study (this was across two sites), and finally (4) the overall PI.

Consider a  full length life-sciences article. It might have 8 figures, each of which might be composed of 6 panels. There will also probably be at least the same amount of supplementary material (often more) that is available from the journal's website, but not in the main text of the paper. Thats 96 figure panels. Now consider that even the most simple experiment takes at least 2 weeks, and will have to be done in at least triplicate. So, that's a minimum of 6 weeks per panel even for easy experiments, or 576 weeks worth of experiments.
A grad student in the UK gets 3 years for data collection, or 141 weeks working 47 weeks a year. So that's 4 PhDs worth of data. Then their will be multiple PIs, maybe because there are different sorts of data being collected, maybe because no one PI has that many PhD students doing nothing.  There will probably be at least one, but probably more lab techs.
If there is animal work there will be an animal house tech, if there is clinical work there will be a clinical team (Doctor, Nurses, counsellors etc).
Also consider that the people who generate the data might no be the people who can analyse and model it, so there will be at least one biostatistician or bioinformatician + their PI.
Of course all this assumes that 1) everything works smoothly, nothing needs doing again and all the experiments you do are the correct ones first time and they all make it into the paper, which is frankly preposterous; and 2) That you are able to wait 3 years to get everything together - if you need it in 1.5 years then double all the numbers above.

It really depends on the field and the culture of that field. For example, I find that in several fields, most projects are run by a PI and require many student and professional researchers to gather data, perform experiments, analyze results, etc. As a result, the entire team usually ends up on the paper, even if there is a lot of variance in the amount of contribution that each member of the team brought to the paper. This is common in fields like biology, chemistry, biochemistry, psychology, and physics.
Having many coauthors is also common in fields that are interdisciplinary and straddle application and theory. For example, in statistics, you often find that there are a multiple statisticians collaborating with multiple professionals in other fields, like biology or medicine. The statisticians develop the statistical models and do the data analysis, while, say, the biologists help provide necessary scientific knowledge and expert opinion.
Another good example is when a paper began as a student research project, especially for students new to research. You often find that several students are put on the same project to make it easier on the students and provide a smoother transition into research.
A more specific example is the following. I know (by observation and word of mouth) that the culture in many computer science departments to place a lot of students on a single paper in order to pump out a lot of papers quickly. I am not sure on the exact the reason for this. But it is not unusual to find computer science PhD students with many publications, with many of those papers having several coauthors.


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Clear colormap for figures when printed?
I often produce figures with a surface with quantitative data attached, plotted in a heatmap type style (see figure).
Previously, I've used the default matlab colormap for this but I recently noticed how terrible this is when printed black & white, the high and low colors appear the same.
Is there a better/recommended colormap I should use to improve clarity when printed.
Related: Are there good reasons to avoid using color in research papers?

Does Matlab have grayscale heatmaps? That would be probably what you're looking for, since it would plot from dark to light rather than based on brightness.

The new default colormap in (yuck) matlab has exactly this property - see this post

You can use the colormaps with a monotonic lightness increase like gray, summer, bone etc. I personally only use jet when I have data equally spread in negative and positive around 0 (e.g. phase data).

Edward Tufte's classic The Visual Display of Quantitative Information covers this IIRC. Well worth reading.

Obviously I didn't realise parula existed. I should probably upgrade my matlab installation. If someone can give a good explanation of why it is better I will probably accept that. If no one does I might have a stab at it in a couple of days.

In matlab use colormap(hot(val)), where the val is the number of shades for the given colormap, also use "shading interp", when it is possible, to make the transition from one color to another smoother.

UPDATE:2
Use perceptually uniform color-maps when possible. I suggest to use this nice toolbox in Matlab (colorBrewer) or to use the new perceptually uniform colormaps from python matplotlib (also available in Matlab).
Matplotlib's new colormaps look like this:

UPDATE:
If you use Matlab, and you have access to Matlab R2014b or newer, use the default colormap: parula. They designed it so it is a good colormap, not only for b&w printing but also for correct data visualization (i.e. it doesn't add features due to the choice of colour, as jet does)
Explanation: http://blogs.mathworks.com/steve/2014/10/20/a-new-colormap-for-matlab-part-2-troubles-with-rainbows/
Parula looks like this:

Original answer
Use the "hot" colormap. If you use Matlab this is one of the built colormaps. I believe than in numpy and other languages you also have this colormap included.
You can see in the image below, how the commonly used "jet" , the "hot" and a "gray" colormap are seen in grayscale. It can be seen that the "hot" colormap is quite good both in colour and grayscale.

For clarity: in the figure at the top, the two leftmost panels are jet (terrible) and parula (matlab's new default); the other four panels are the new colormaps released for matplotlib that have superior visual properties (they're perceptually uniform in both color and black-and-white). Parula is a fine colormap, but somewhat less optimal in its native form (but then, I would say that ;-)); and it doesn't even attempt to be perceptually uniform when printed in black-and-white (see https://bids.github.io/colormap/ for analyses). Pretty much anything is better than jet.

Thanks to those who pointed out that Parula is the new default Matlab colormap and solves this problem nicely. I will give a more general explanation of why this problem arises. A very good series of articles about colormaps can be found here.
I'll consider the colormap as a series of colours in Lab space, for reasons which will become clear shortly. In Lab space L represents the colour's lightness and a and b give the colour. Therefore we can view converting to greyscale as taking only the L component (with a=b=0). Therefore to convert well into greyscale our colormap should be monotonic in L and ideally approximately linear.
The 3D color inspector plugin for imageJ provides a handy tool to visualise the colormaps in Lab space. Looking at the Jet colormap (the old Matlab default) in this way the problem becomes clear. Jet is not monotonic in lightness and approaches maximum lightness somewhere in the green/yellow range. Therefore, when converted to greyscale the two ends of the map appear dark while the centre is light coloured.
Compare this to the parula colormap, which is monotonic in lightness. If you do further analysis you can also show that it is reasonably linear in lightness. The conversion to greyscale will therefore be pretty good.
There are many other colormaps which also have this property of monotonic lightness, in particular most monotone maps.  However, it is also advantageous to maximise the distance between colours in Lab space to increase clarity when viewing in colour. Monochrome maps are relatively weak in this respect as they have a much more limited range of ab values than rainbow type maps.

Perceptually uniform colormaps in Matlab: http://uk.mathworks.com/matlabcentral/fileexchange/51986-perceptually-uniform-colormaps

I would +10 if I could. Remember that while gray printing is one thing, printing on colour (especially laser) printers is another. These printers are known for giving "hard" pictures -- with high saturation and low quality on luminosity; basically, you're only left with hue, and parula seems to be quite good in this.

I think the default colour map is not that good for most cases, since having essentially four different colours can make it look different than what it is. A very clear example of what I say (and a bunch of rants why one should not use MATLAB).

See the yellow stripe? It is essentially an artefact of jet, and not a property of the data (more on the post).
For a better option, I would suggest Color Brewer, that can suggest you different colour schemes safe for black and white, colourblind friendly, etc.

As per ff524's comment, jet is no longer the default colourmap of Matlab (hurrah!) as of R2014b. Unless there's good reason to use another map, the new default parula looks great.

It's also trivial to change the color map to something more appropriate.

Isn't the question about downgrading color colormaps to black and white? Can you update your answer to address this issue?

Suggesting to abandon Matlab because you dont like the default colormap is like not going to an Italian restaurant because you dont like pepper.

@BillBarth the last paragraph does exactly that.

@AnderBiguri that is not my blog, nor my opinions, but if you read the rest, it is like not going to an Italian restaurant because you don't like pasta, pizza bread, tomato...

I can't read your "bunch of rants", your blog is login-walled.

For a free (and maybe nicer?) alternative to Parula use Python matplotlib's Viridis.  See this fascinating talk for its development:

Nicer for sure ;)

Image from here.

The figure examples pretty much says it all, if you look at how the different hues are translated into a grey scale you will see that a two-tone scale going from a colour rendering dark in B/W to a colour rendering light in B/W would the solution. But, this is not much different from using a monochrome scale from light to dark since the boundary when colour one and colour two may only be clearly visible in colour and not in B/W.
There is thus reasons for trying to solve graphics in B/W until it is clearly impossible to solve what you want to visualize in any other form than with multiple colours. Back in time, one would always argue that copying colour papers would result in poor B/W copies is not so relevant anymore since it is possible to use colour copiers, a combination of scanning and printing in colour or downloading a pdf to print in colour.
So while colour is all around us there are still reasons to consider B/W as a primary choice. Colour blindness (as mentioned in another reply) is one such reason. Plotting software, be it rudimentary as Excel or more advanced as Matlab
forces colour in cases where it is not necessary.

The chosen answer mentions the 'hot' colormap, but I tend to find this is often too bright. I find that the 'cold' colormap (top) often works better.

Also no one has mentioned monochrome colormaps (bottom), which are just like grayscale but without the gray. These give you the ability to pick any hue you like, which can be helpful for making the colormap fit in with other elements of a presentation/poster/report etc. And you're guaranteed they'll remain meaningful in black and white.
I give more detail in this post

I could write an answer to your question but why rewriting when the exact problem you describe and its solution have been beautifully explained in this webpage
https://cran.r-project.org/web/packages/viridis/vignettes/intro-to-viridis.html
In there you will find the following colour scales:

Viridis
Magma
Plasma
Inferno
Cividis

While this link may answer the question, it is better to include the essential parts of the answer here and provide the link for reference.  Link-only answers can become invalid if the linked page changes. - From Review

Welcome to Academia.SE! I agree with @Flyto. Would you please expand your answer to be independent of the link? Otherwise it is likely to be down voted or removed. Thank you!

Hello. Apologies if it sounds a bit dry, but 'the essential parts of the answer' you would like me to include are long and wide, and explaining them here would take me a long time - which I wish I had.
My intention is to provide anyone who read this with a piece of information which may lead them to read a very compelling answer written somewhere else.  If you are interested in the subject, and the link is dead, the chances are you will google the list of colour scales I mentioned and find a replica of the 'dead link' in another webpage.

If you still consider this an 'unacceptable answer', then my answer can be removed.  People who could benefit of this piece of information may not come across it though.  My only intention is to share the info, because it helped me back in time.  Hope this helps.

license: Creative Commons - Attribution Share-Alike - https://creativecommons.org/licenses/by-sa/3.0/


text:
Are there any international standards, laws, or governing bodies that deal with allegations of plagiarism?
Consider a case of plagiarism like this one. The allegation in this case led to formal investigations, articles being retracted, and researchers being sanctioned.
Who is the responsible body for launching and conducting these sorts of investigations? Are there global laws that determine norms of ethical conduct and describe the procedure for dealing with breaches?

The question is about how plagiarism is treated nowadays by research institutions. Are there books, seminal scientific articles, or expert institutions on the subject? In the last 2-3 years I have studied the subject and my perception is that there is little specialized literature on the subject. What emerges in debates on this topic are recommendations from Professors or researchers, while publishers refrain from a serious discussion on the topic. A historical point of view is given by Wikipedia (https://en.wikipedia.org/wiki/Plagiarism), but it is not enough for a research about the theme.

I took a stab at editing this. Note that we do not recommend books (these are "shopping questions"), and that the question text should not contain meta-discussion about the question being closed. After removing those elements, I believe your question reduces to the above. Feel free to make further edits if needed (though this may affect your odds of getting reopened).

@cag51, my question is similiar with this:
https://math.stackexchange.com/questions/1009371/book-recommendation-for-analysis-problems/1033177#1033177
But the content is not Mathematics, but plagiarism. I not consider any question about book a "shopping question". I consider your comment ("Note that we do not recommend books (these are "shopping questions"), offensive and intimidating. Stack Academia doesn't seem to be in line with Stack Overflow, or others.

Book recommendations are certainly borderline for the SE format, as there in many cases there can be a lot of possible answers and there is no clearly correct answer (and it’s not possible to know before the answers come in). Stack Exchange sites can decide individually whether such questions are suitable for them. That being said, what you are looking for is probably not covered in a book and instead you want any reference which addresses a narrow topic – which I would consider a [tag:reference-request] and fine.

@Student: cag51 was referring to a policy of this site (see here for details). Clearly, you are free to agree or disagree with this policy, and of course you are also free to agree or disagree as to whether your question falls into the category "shopping question". But I seriously don't understand what about cag51's comment would be "intimidating" or even "offensive".

No, these issues are typically not handled by international standards/laws/governing bodies. Few of these even exist: academia is not organized hierarchically at the international level; for many countries it's not even organized at the national level.
In the example you cite:

Five years ago, OSU, in Columbus, opened inquiries into papers

In this case, you can see that it's the university (Ohio State University, located in Columbus, Ohio, US) doing the investigation, because the person accused is their employee. There are other Q&A on this site asking "what's the use of a university to a researcher" or some variation on that theme; well, here's one of the uses. By working at an institution that values its own reputation, researchers open themselves to investigation by the institution that they work for. This gives everyone else that works at the institution some additional credibility, in that their job is at risk if they break the rules.

Concerns about work from Croce’s laboratory first came to widespread attention in 2017, when The New York Times reported on allegations of research misconduct

The media also has a role; by reporting on these allegations, the newspaper induces others to investigate. In this case, it seems some OSU investigations occurred before the newspaper report, but the report also induced the university to investigate further. Media attention also may attract other people who were wronged by someone and perhaps have a stronger case than they realized once their accusations are pooled with similar accusations by other people.
Nature is also acting in more of a media role (rather than as an academic journal) by posting this article.
The primary bodies that deal with accusations of plagiarism in academia are:

Universities and other academic institutions, who have the power to fire researchers, deny students credit or graduation, etc.

Journals and publishers, who have the power to retract articles in their publications.

Media at large, including newspapers, who can induce others to act through public attention and embarrassment

Granting agencies, who can require academic institutions to uphold ethical standards and withhold funds from individuals or entire institutions that fail to do so. The Office of Research Integrity mentioned in the article would be an example.

Professional societies, who have influence through journals they control and any standards they set for membership. While these can be international, they are unlikely to have any legal authority. They can kick people out/prevent them from being members, prevent them from participating in their conferences/journals, and bring attention by other entities with more direct control.

Occasionally, some forms of plagiarism are also violations of legal constructs like copyright. There may be international norms, but these violations are ultimately dependent on whatever legal system has jurisdiction, typically a national or local court system. However, even these legal violations are far more likely to be resolved at another level first, such as a journal retracting a plagiarized article. Legal action is expensive and complicated and typically reserved for when other mechanisms (including threat of legal action) somehow fail.

The only international bodies I can think of are those that have a role only in setting guidelines/suggested standards; it's up to those who wish to uphold these guidelines to actually do so. An example would be COPE.

I'm tempted to edit in "The Ohio State University"

@AzorAhai-him- They can have it as long as they're okay with changing their acronym to TOS-U, affectionately, "the tos'ers".

@BryanKrause, OSU has always been the tossers (graduate of a sports rival).

@Buffy As a graduate of two of them, I must doubly agree ;)

The bullet point about professional societies ends very abruptly.

@Anyon Thanks, must have been distracted mid-thought and didn't realize...

edit because of comments
No, but there are national rules that are internationally harmonized and two general remarks why:
Nation States and to a lesser degree supranational bodies like the European Union are entities that make "laws", that is enforceable rules because of the existence of the rule of law. Not all states are fully committed to the rule of law. However, international treaties between states may establish rules, guidelines and regulatory bodies. The implementation and proper functioning are less likely.
Some incidents of Plagiarism - that is: taking ideas and words of other people and claiming them to be yours - is forbidden for example under copyright law, so only countries with legislation about copyright (may) have bodies to turn to. There are international treaties that try to establish minimum standards internationally: Have a look at the World Intellectual Property Organization (of the UN) that administers those treaties, you'll find information on member countries and there rules there.

Plagiarism and copyright violations are two separate concepts. Not everything we would classify as plagiarism is a copyright violation and vice versa. Examples of people who got into trouble because of plagiarism were not punished under copyright laws, but their employer (the university) imposing some sanction or firing them or a journal retracting the paper. If a court was involved, then it was because purpotrator objected against his/her punishment...

...  So laws are not directly relevant for cases of plagiarism. Where the law does become relevant, then it is labor laws that limit the ability of universities to punish people who committed plagiarism, rather than laws that directly deal with plagiarism.

That is only partly correct. Many types of plagiarism are forbidden under international copyright treaties. There are of course some parts that are not, for which other laws may apply - which is, why my first remark is correct.

Conflating plagiarism and copyright is just a mistake.  They are very separate issues.  It makes little sense to discuss them as if they are the same, or even similar.  Copyright violation can occur even with citation, and plagiarism can occur with no copyright violation.  Mixing them will just confuse people.

I disagree because of the overlap. Remember the question: It was asked for "global laws" and governing bodies. In my country, you are forbidden by copyright law to plagiarize (in the sense of copying without citation) and you can be prosecuted by it. I should know because copyright was my phd topic. Some universities make you sign copyright contracts next to normal work contracts (as did mine).


text:
Main result in the first figure
I am planning to submit my paper to a journal. But as I circulate my draft paper to my student peer, one of them ask me why I put my main result in the very first figure. I was silent without any argument.
As far as I know, there are no specific rules against putting the main result in the first figure, but I also could not find a paper that put their main result in the very first figure. Is there any reason why we do not put the main result in the very first figure?

Why not? It's the first thing that people look in a paper!

I think based on the Mr./Mrs. @ouic answer, the paper needs to follow a logical orders so the reader can follow the story of the paper.

It depends. There are no rules, but you need to make sure that your paper follows a logical order and is easy to understand for the reader. The reader wants to see that any conclusions you draw logically result from the measurements or calculations. This often leads to the main result being presented at the end, but that has never been a requirement or convention.
There is no a-priori reason to not put the main result in the first figure, but there may be underlying problems with the paper, and moving the figure to the end could be (part of) the solution that your peer proposes. Impossible to tell without reading the paper.

Thank you for the answer. It is crystal clear and I absolutely agree.


text:
How to decide whether and where to publish your research?
I am writing my thesis in applied mathematics.
I developed two numerical schemes that seem to have practical applications in many fields and certainly have bright prospect for mathematical software.
I checked many existing resources and could not find anything similar or better.
How do I find out if this worth publishing? And if so how do I find the journal?

You're lucky:

I am writing my thesis

If you're writing your thesis, you have a thesis advisor. They would be extremely well-placed to answer your questions - not only do they know your work well, they have a vested interest in seeing you succeed. They are also well-placed to act as your mentors in academic issues such as this.
You can ask them both about whether it's worth publishing and where to publish it, and what they say will likely be a better answer than anything an outsider can write.

@NotaChoice Why don't you trust them? Why have an advisor you don't trust? Instead of a professor in the field you work in who is closely familiar with your specific work, why rely on internet strangers who aren't in your field and don't know your work?

@NotaChoice If you don't trust your advisor enough to ask them these kind of questions you have much bigger issues than finding out whether and where your ideas could be published. How do you plan to write a thesis with this advisor?

I deleted my earlier comments as I used the wrong word, I meant but what if I don't believe them, trust is no issue.

@NotaChoice you should believe them because they're much more likely to be right than wrong. If you are really skeptical I suppose you could ask another professor in your department whether your supervisor is right (or ask another question here), I guess.


text:
Does thesis or publication in a foreign language hinder the prospect of it?
Background: I'm a undergraduate student who is planning on going out to Canada to do my master’s.
Since I'll be applying to universities there I'm stuck thinking if writing my research thesis in Lithuanian might diminish my chances of getting into good universities.
Are there any pros of me writing the thesis in English?

Writing in English will make your work accessible to more people. Otherwise translation would be needed.
Eventually you will probably want to publish in a more common language, but you can fairly easily avoid issues in the short term by writing your CV in such a way that you provide the original titles of papers/theses along with English translations.
One advantage of writing originally in English, provided it is grammatically correct, is that it will be easier to convince people in admissions that you are conversant in English, which will be needed most places in Canada and everywhere in US.

Note that in some fields, auto translation like Google Translate does a terrible job; math, for example. It may be better in other fields with a bit less technical terminology.

Google Translate does a terrible job; math, for example --- It's a lot better than it used to be, which I'm sure you know, but I thought you might be amused with the types of translations my early uses of google translate gave. Here's a made up example, but the actual examples were just as bad: "every segmented lower whole is surrounded" for what should have been "every measurable subset is bounded". And no, this isn't intended to be a realistic assertion that one might expect to encounter in measure theory -- I just made something up for illustrative purposes.

Is there any field of any human activity where Google Translate does not do a terrible job? Or is it better when translating stuff into English? When I am being forced (by Google) to read stuff like app description auto-translated from English, it is all just garbage.

@VladimirFГероямслава, my guess (just a guess) is that philosophy or history might be reasonable with a bit of tweaking. I use it here to translate questions presented in other languages and it works for that because there is little technical jargon.

authors: Vladimir F Героям слава


text:
Does Stack Exchange answers have the same validity as pre-print sites like arXiv when proving priority?
Say I have done some simple calculations in an answer to a Stack Exchange question, which after a quick search does not have any existing papers detailing the same procedure, or that the few existing papers do not have entirely correct results. However in the moment I do not think it is worth my time to type up a complete pre-print and upload to arXiv, and the idea still needs some developing.
If I ever wish to publish the results, can I use this Stack Exchange answer as proof of my priority for coming up with the idea/calculation, instead of more conventional pre-print sites like arXiv? Will this be respected if there is a dispute over priority?

If your idea is so simple that you refer to it as “simple calculations ... that I do not think it is worth my time to type up” then your question is moot, since the value of the idea is negligible, and no one will ever bother disputing that you came up with it when you did. In that sense, it is also the case that no one will question that you have priority (so your priority will be “respected”). But this is of no consequence: you will not gain any practical benefit from having people recognize that you have that type of priority.
Moreover, in my experience as a frequent reader of MathOverflow and some of the other Stack Exchange sites, the nature of simple calculations of the sort that get posted on Stack Exchange is that they are almost never truly original. It is much more common that they are either identical to or minor variations on well known ideas (or sometimes not so well known ideas that nonetheless experts recognize as being standard in the literature). So it’s quite probable that your priority will be superseded by someone else’s results anyway.
The bottom line is that your question appears to be based on a misconception that simple calculations can be more valuable or original than they typically are.

This seems to be exactly the scenario the OP is concerned about: That simple things indeed typically are not noteworthy, but the occasional pearl is among them. Being rare isn’t a defect, it’s the point.

It would be dangerous for a person to depend on a site like SE for such a thing. While you might be successful in such a claim, note that posts here are infinitely editable, as was your question. This can occur at any time and by most users. Posts are also subject to deletion and some disappear quite completely. So, your claim might vanish by the time you want to make it.
Use a site designed for the purpose you have and that promises permanence and traceability.

To be fair, the change history would indicate when and what edits were made to the post. Still, as you note, SE is not the right tool to use.

@JonCuster, policies can and do change and the management makes no promises that would safeguard the writers interests.

It is pretty clear from the past (more than a) few years that we can't trust anything management says about SE. But the content will live on in the data dumps somewhere on the internet.


text:
Should I report a review I suspect to be AI-generated?
We've recently received reviews for a paper submitted to a top (STEM) journal in my field. Most reviews were helpful, but one was particularly sloppy, heavily misunderstanding our paper and making false and bizarre claims about its contents. Based on this particular review’s format and content, I heavily suspect it was written by feeding our paper to ChatGPT and making minimal edits, but I have no proof of this. The review also penalizes our paper for reasons the journal guidelines explicitly say are not acceptable.
The overall decision was revise and resubmit. I have no problem with the decision, but I am concerned about receiving this kind of review again post-revisions. I wanted to ask if it is worth flagging this particular review to the editor ahead of time, perhaps asking for a different reviewer, or if the review should be ordinarily addressed like one usually addresses reviews during revisions. I feel like a journal of this caliber might want to be aware of such reviews, but I am wary of making the claim that it was written by ChatGPT without proof, as this might be rather damaging/unfair to the reviewer.
I also wanted to ask if anyone has experience with possibly machine-generated reviews for their papers, and what they've done about it.

It sounds like hallucination if it is chatgpt. Do you feel you could challenge the quality of the review on its own due to bizarre claims (assuming it was written by a human)? When challenging it on that merit, you could suggest it was written by chatgpt?

The review's claims are in some parts easily challenged (they are plainly false), and in other cases unintelligible (as in, I could not find an interpretation that makes sense--it reads like a ChatGPT-esque hallucination). The unintelligible stuff is very hard to address because it "reads" like it makes sense, but doesn't if you are familiar with the paper. I can address this in revisions, but I am wary of my "rebuttal" coming off as overly aggressive/negative (I don't think they'll take this kindly) and I am wary of receiving the same type of ChatGPT-esque review in response.

Have you tried feeding your paper into ChatGPT yourself, to see whether it agrees with the suspect review? That might corroborate your suspicions.

@SimonCrase each run is seeded differently so in practice chatgpt won't tell you the same thing twice.

I feel like a journal of this caliber might want to be aware of such reviews, but I am wary of making the claim that it was written by ChatGPT without proof, as this might be rather damaging/unfair to the reviewer.

You don’t need to make a claim. You can just point out that the review feels machine-generated to you and list the major reasons why you think so. It is totally fine to report reasonably substantiated suspicions without rock-solid proof. The journal hopefully has more experience and resources to check whether a review was machine-generated, and they need to independently check any accusations made against reviewers anyway. If they blindly act on your hunch, this would be their fault, not yours.
I would even go further and argue that given sufficiently good (although not compelling) evidence, it’s your duty to report it. Such a reviewer could already have done considerable damage or do so in the future.

I wanted to ask if it is worth flagging this particular review to the editor ahead of time, perhaps asking for a different reviewer, or if the review should be ordinarily addressed like one usually addresses reviews during revisions.

I cannot say how bad this particular review is, but addressing a bogus review can be a major waste of time. Also, investigating your suspicion will take some time, which may delay the process if it’s only done upon resubmission.
I would therefore contact the journal immediately in hope that the review is voided. I don’t see a benefit of explicitly asking for a different reviewer though. It is up to the journal how to react to this.

I wouldn't expect that the journal would care about the generated review.
The main choice would be to the specific editor

@KY: Suppose a journal makes a bad decision based on an obviously AI-generated review, e.g., rejecting a decent paper. Suppose the authors go public about this and it gets verified. That would considerably harm the journal’s reputation. So, yes, I am quite confident they will care.

@KY In addition to the very applicable comment by Wrzlprmft, the whole purpose of peer review is to allow scientists to check the work of other scientists.  A generated review subverts that purpose.

@BobBrown I don't think this follows logically. If models were smart enough (yes, they aren't... yet), one could argue it would be irresponsible of reviewers not to discuss a paper with a model, and there could then be cases where an auto-generated review resulting from that discussion would suffice to justify a reject decision (or maybe even an accept decision). This would be similar to how everyone who is serious about analysing a chess game uses engines, and often engine output is sufficient to evaluate a particular move to everyone's satisfaction.

The best approach may be to challenge the review directly with the editor on grounds of quality, prior to your revision, and assuming the review was written by a human. During that challenge you can focus on the false/bizarre claims and the issues that the review raises that the journal guidelines say should not be reasons for penalisation. During this you can raise the idea of it being chatgpt, but it sounds like the review is weak enough to challenge by itself.
I'll change the framing question, to point out that there is nothing automatically wrong with a chatgpt review, just as there would be nothing automatically wrong with a monkey's review. So long as its checked by a someone who read and understood the paper to make sure its correct and complete and would be willing to put their name to it. The method the text is produced, human/AI/monkey, could be treated as a black box, a brilliant and insightful review created by chatgpt would still brilliant and insightful. So the focus of the challenge should be on why the review is poor and should be ignored rather than that you suspect it was written by chatgpt (or written by a monkey).
Of course, submitting a sub-par AI generated review is insulting since it is a waste of everyone's time. It would also probably be quicker for a reviewer to write it themselves rather than try to tweak current AIs to get a meaningful review from them. This may not be the case in the future, but current AIs tend to write generic stuff and lack any understanding of the kinds of topics that are on the cutting edge, such as research.

I'll point out that there is nothing necessarily wrong with a chatgpt review, so long as its checked by a knowledgeable reviewer to make sure its correct and complete. – This seems to ignore the fact that the major time sink of a review is reading and understanding the manuscript, which the reviewer checking a machine-generated review would have to do anyway. Also, I doubt that genAI can read plots or detect more than very superficial problems with a manuscript, etc. So the potential time save seems to be negligible. (Mind that this is different for genAI polishing the language of a review.)

@Wrzlprmft, I 100% agree with this, the point I was trying to make is that if it was written by chatgpt and verified by someone who took the time to read and understand the paper who then verified that chatgpt had (somehow) managed to write a valid review then there is nothing wrong with it. But the chances chatgpt actually does do this is negligible at its current level of technology. So its better to target the review as if it was written by a human, then suggest at the end that you suspect chatgpt was used, which wasted everyone's time. I'll edit the answer to make this clearer.

LLM output is harder to verify than random output, because it's specifically optimized to be superficially reasonable sounding, but without regard for actual correctness, since correctness is too hard to quantify during training. In other words, it's much more likely for even an expert to falsely conclude LLM output is correct than random output.

@Vaelus LLM output is harder to reject as invalid, but not necessarily easier to accept as valid, than random output. All the superficially reasonable text that LLMs generate are also generated by uniformally random output, but just much much slower. The point I'm making is that when challenging AI reviews, its likely that the review will not be correlated with journal policy and paper content, just as a terrible human review would be. In theory an AI may come up with the perfect review (or a reviewer may guide the AI to such a review), but this is unlikely.

What’s the point of the second and third paragraph? We all seem to agree that the probability of a good machine-generated review is negligible. However, a reviewer submitting machine-generated reviews is a big problem as it may not be spotted every time – even though the reviews are bad every time. (For example, suppose the machine generates a positive review consisting mainly of a summary of the manuscript and stuff so minor even an AI can find it.) Ignoring that a review is very likely machine-generated and addressing only its text is borderline irresponsible (and a waste of time).

The entire point of a review is to be the opinion of an expert in the field, not the opinion of a monkey, robot etc. that the expert just happens to agree with. It very much does matter that the expert writes the review rather than merely signing off on it.

Note that the US National Institutes of Health prohibits the use of AI in peer review. Beyond what has been mentioned here, theft of unpublished confidential information is also a concern.


text:
Does the course number matter?
I have a doctorate already and have circled back to complete an MBA. My doctorate is in another discipline.
My MBA program just told me that, rather than using the proposed course numbers for an analytics track, they will be using the repeatable "Special Topics" 5000 number.
For example:

MBA 5510 Data Warehouses will now be MBA 5000 Data Warehouses
MBA 5520 Advanced Queries will now be MBA 5000 Advanced Queries
MBA 5530 Advanced Data Mining will now be MBA 5000 Adv Data Mining

Our course catalog lists MBA 5000 as "Special Topics" and it is shown as repeatable. I am not sure whether my transcript would reflect special topics or the original course title, but I will end up with four instances of the MBA 5000 course number on my transcript.
As someone involved in an academic career, should I be wary of this change? Will other institutions scrutinize the use of this repeatable "Special Topics" course number and/or name?

Anyone who cares what these courses are will know what a Special Topics course is, i.e. that it's a course placeholder in the scheduling and reservation system for courses that may be taught infrequently or even once, and ask you about the topic(s). This is a very common construct at universities in the US that I am familiar with, and shouldn't be a problem for you since you will have an opportunity to tell them what the topics were.

Yeah, in the schools I've taught at "placeholder" course numbers are common.  We had, for instance, a WNM-500 course that was a completely different subject from section to section.

You could start by checking with the registrar's office to find out if the transcript will show the specific topic of each of these courses or whether it will just show "5000 Special Topics."
e.g. on our campus you'd actually get "5000 Special Topics: Advanced Queueing Theory."
I doubt that anyone would care about the difference between a special topics course with the topic of Advanced Queueing Theory and a regular course in Advanced Queueing Theory if the transcript showed the specific topic.
It's possible that someone might be concerned about your taking "Special Topics" with no specific topic mentioned, but even that shouldn't be much of an issue if you're applying for an academic position.

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