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Importance of Variety for PhD Recommendation Letters
I am a senior getting ready to apply to PhD programs in Applied Physics (I currently am located in and plan to attend grad school in the US). I am currently working a co-op at a large company in a small, very research-oriented and very academic group doing research that is directly applicable to what I want to study in grad school. The group lead and another researcher (both hold PhDs) have agreed to write letters of recommendation for me, and the third will be from a professor at my university.
Today I was told that it could be more beneficial to have only one letter be from the place I work now (Company A), as having two letters from the same place (but different people) could be seen as redundant. I do have a former employer from "Company B" I could ask (who does have a PhD), and the main work at Company B is tangentially related to what I want to focus on in grad school, but I feel that while the letter will be strong and positive regardless of who it comes from out of these, it could be more beneficial to have a second letter come from Company A. I say this because I have been here longer (15 months vs. 6 months at the previous company) and have therefore taken a larger role, everyone I work with now publishes regularly unlike at Company B where the work was more focused on R&D for product development, and both letter writers at Company A are well-connected in the field and more specifically this particular research topic. Additionally, I feel that the writers at Company A could comment more specifically on my research inclination, while at Company B it would be more focused on technical ability and hard work. Finally, I haven't been in touch with my employer from Company B, and while it would be straightforward to reestablish contact (and I'm sure they would respond positively), this could be considered a disadvantage.
My question is: Despite the fact that a second letter from the company I work for now could be slightly stronger compared to a letter from a previous employer, could having variety in my letters outweigh this?

Have you been publishing with Company A?

We currently have a manuscript in preparation and may have one more by the time I submit applications, but at this moment no.

We currently have a manuscript in preparation and may have one more by the time I submit applications, but at this moment no.

Generally speaking, strength outweighs variety, with a caveat. If your two letters from Company A are both vary strong and they say different things about you, that's the way forward, especially if you think that the letter from Company B would be quite weak. However if both letters from Company A comment on your same attributes, discuss the same responsibilities and outcomes, and essentially tell the admissions committee the same thing twice, you are better off with the weaker but different company B letter.

This seems correct in this context, though I normally prefer letters from academics for undergraduates in the US applying for graduate education. The beginning of the graduate program isn't normally heavily research focused in the US (not so in Europe, of course). But here I think the advice is good. Strength trumps variety. But the letters should complement each other rather than say the same things.


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Is there difference in the target audience addressed via a conference paper and via a journal paper in field of Computer Science?
As per my understanding in both scenarios we write our research addressing the research community. Yet at what point we decide to write a conference paper or a journal paper? Is it via looking at the target audience. If so what are the differences in the audience?

The issue is not just the "audience", but rather the "visibility".
Any venue - be it a journal or a conference - has some specific topics that define the "expected audience" of the people that will read the articles published within it.
However, in conferences you know in advance that at least N people will look at your work, because it has to be presented to the people attending the conference (at least before Covid - with online conferences, I had the impression that everyone was attending just for making their own presentation, and then go do something else). These people will mostly be researchers, but there will also be practitioners.
Because of this reason, there are different styles that must be adopted when deciding if the paper is going to be published in a journal or a conference. In conferences, you are usually restricted by the amount of available space, and it is important that the "claim" of the paper will make an impact now, or that will at least raise the interest of the people attending the conference; this may favor a more "high-level" approach on a trendy topic, where technicalities are not as crucial.
On the other hand, in journals you have more space available, meaning that you can go more deeply in discussing the technical details of your proposal. This may make the paper more interesting for future and long term research.

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How can i cite a kaggle dataset in ieee conference paper?
I want to know how can I add a dataset link as a reference in the IEEE conference paper. I used a dataset for my works and then I described the dataset in the paper. But I don't know how to cite the Kaggle dataset as a reference.
Update:
I got a solution and here is the link. There was a solution and that was:
[Dataset creator's name]. ([Year & Month of dataset creation]). [Name of the dataset], [Version of the dataset]. Retrieved [Date Retrieved] from [URL of the dataset].
Is this ok for IEEE conference format? Thank You.

A quick google search will lead you to a forum post on kaggle where citing datasets is discussed: https://www.kaggle.com/general/46091

hlw @Louic, Thank you. But I had a little bit confused about the format of the following citation. Because there might be a difference in IEEE conference format for the Kaggle dataset.

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Why aren't more research papers/textbooks written in a "thought process" style?
This may be a weird question, but I often wonder why the default way of writing about a given methodology is describing the method as if it "fell out of the blue". Admittedly, not all research work is written like this and perhaps this is not an issue for very experienced researchers/practitioners, but I believe it would be much more insightful if authors wrote the steps that led to the specific decisions when designing a new method. So, instead of describing a new method, the author would write the problem and motivation (this is usually done), and then elaborate on the thought process that led to the final design choices. I don't mean hand-holding on basic stuff, but to describe things like "we wondered if there was a way to solve X. Y is a popular method to achieve Z, a property required when solving X for the reason K. However, Y needs to be tweaked in the following manner so that it also asymptotically satisfies W"  or "we tried this way of solving it, but it did not work due the following reasons [insert reasons], so we decided to try this instead". Was a certain property coincidentally satisfied by your decision choice, or did you reverse engineer a method that satisfies that property?
This may seem like a childish idea and most people probably don't have the time or interest in such a writing style (or may even feel insulted by overly detailed descriptions). However, I believe it would be a much more valuable contribution to science because:

Researchers would learn from each other different ways of thinking about a research problem and the strategies to solve it
Researchers interested in building on the presented work would know which ideas did not work (preventing them to pursue dead-ends), and if they know better ways of solving a particular sub-problem they could easily improve the method.

What are your thoughts on this?

This seems entirely opinion based. But researchers aren't cut off from that "process" stuff. It just appears elsewhere than in the papers themselves. Moreover much of it is irrelevant to the results and could even be misleading.

With exceptions for reviews and the like, journals typically have page limits, e.g., eight pages. Even with online supplements available, conciseness helps readers keep their heads above the rising tide of publications. Maybe.

I've heard of honors Calculus classes that do teach this way, but are very confusing for the student. Also, looks at books that do. For example, a Radical Approach to Real Analysis does describe the history. Last, the history of science field often covers the how science discoveries occurred and the thought process.

@Buffy Could elaborate on your answer? Where, exactly, does it appear? And how could describing the strategy used to solve a problem be misleading? If anything, I believe it could help clarify why things were solved in a particular way instead of other more obvious one.

I haven't given an "answer", actually. Scholars have discussions and answer questions about their work all the time. Classroom, conferences, invited talks, blogs, articles, letters, etc. I can for see a situation in which a "discussion" of "thoughts" leads a reviewer to accept something they should not, perhaps due to leaps of logic or whatever.

@Buffy "I can for see a situation in which a "discussion" of "thoughts" leads a reviewer to accept something they should not, perhaps due to leaps of logic or whatever." Isn't it the case that more transparent reasoning about a new method would more easily expose said leaps of logic rather than solely relying on self-reported results in a medium where only positive results are encouraged to be published?

There's been a lot of prior discussion of this question in the scientific literature. See responses to Peter Medawar's Is the scientific paper a fraud?, for example. I also recall a similar sentiment expressed independently.

The science should stand on its own. If the methodology is valid and carried out faithfully then something is learned. The "why" is irrelevant to the result. And learning that something is not true is just as valid as learning that it is. Graduate students are too often misled to thinking that if they don't get "positive" results then they have failed. A negative result is a valid result. In science, we don't set out to "prove" our hypothesis, but to discover evidence about whether it is true or not. Math is a bit different but there is an equivalent formulation.

@BenTrettel Thanks for the references!
@ Buffy I am coming from the point of view of applied math research. Maybe in other fields this is not as big of an issue or it is actually the standard way the work is presented.

There were numerous discussions of this. As far as mathematics is concerned, you might be interested in looking at an old MO thread https://mathoverflow.net/questions/38639/thinking-and-explaining . The opinions generally seem to range from "It would be nice to communicate the thought process, but it is not clear how to do it without turning the paper into a messy labyrinth" to "Everybody thinks differently from others, so imposing on the readers your way of thinking only confuses them". I believe that some people try to do it now and then, though I cannot think of any success story off hand.

@EdV: Page limits exist in some fields, but they're far from universal.

@Schmuddi Good to know! One nice thing about this particular stack exchange is learning just how varied academia is.

I think it is likely that this would end up with the publication of a lot of misleading post-hoc rationalisations of people's actual thought processes, even with the best of intentions (c.f. Rashomon).

Essentially one wants to see the output of a thought process.  It would give the "appearance of being unprofessional"...

This lecture is exactly about why "thinking process" is very different from "writing process" and two must not be mixed https://www.youtube.com/watch?v=vtIzMaLkCaM

Because we'd come to realize that so many a priori assumptions, data screening, and cherry picking went into the experimental design that we should ignore all the p-values ;-)

they could always do it in an appendix or a blog instead? i figured papers are mainly for the results and methodology

Thought processes are messy.
If you wrote out the thought process behind the typical papers I contribute to, you'd have to distill hours of weekly meetings, circular avenues where the same idea comes up 2 or 3 or 2 dozen different times before it gets incorporated definitively, endless iterations of experimental design, dead ends and failed experiments where everything starts over, side projects that unexpectedly inform a central one, shower thoughts, and pub inspiration.
In a good writer's hand maybe it could turn into a novel, but it wouldn't be the most direct way to explain the main findings of a work and contextualize those results within the literature. As Richard Erickson comments, history of science books often do get into the "process" behind discovery and they can be a great read (Ruse's The Darwinian Revolution is a personal favorite), but it's not reasonable to expect every scientist to go into this level of detail for every paper nor reasonable to expect their audience to invest the time to read it.
I do see "thought process" content in many papers, but at that point it is usually made into a much simpler, linear story to avoid all the meandering.

The "thought process" behind some discoveries went on for a hundred years.

I agree. I am not suggesting adding every single step but rather the core, trimmed down ideas.

@user3653908 Maybe your field is different, then; I see the core/trimmed down thought process often, usually you'll find it in the introduction section. From a textbook perspective I would also say that biology textbooks I've experienced are usually written with history/thought process in mind, at least in part. They usually use a few key experiments to demonstrate "how we know what we know" and familiarize students with scientific methodology rather than just drilling facts. They don't go into everything like this, though, for the same reasons I mention in the answer.

@BryanKrause Right. Maybe I am biased to the ML field where the standard are 8-page conference papers where some new method is described and some properties are shown to hold for this method without any explanation as to how this method was derived.

@user3653908 My cynical view of a lot of papers in the ML field is that there often isn't much thought process, unfortunately. But I am definitely biased in thinking mostly of papers that overlap with my own field where the papers are less "new method in ML" and more "I know sklearn exists, what if I use some of the methods there on this brain data. Brains are cool right?" But less cynically, yes, length limits are surely going to cut down on prose when there is other meat to share. Hopefully they at least cite similar approaches; if not that's just sloppy rather than a style choice.

@BryanKrause Right. I am referring more specifically to new methodology papers and not applied papers.

Even though it's a bit messier, I find it more fair, relatable and educational to read "I did not know, so I asked on SE and Bryan Krause told me that [..]" instead of just stating the final result and including a link in the references.

@Džuris Perhaps, but what I was getting at in this answer is that it is rarely that simple. How deep do you go? Why not start with what brought you to Stack Exchange in the first place? In the opposite direction, isn't "I did not know" redundant? At some point no one knows anything; a citation is a simple format for saying "this is where this knowledge came from".

""I did not know, so I asked on SE and Bryan Krause told me that [..]" That is pretty much what a "personal communication" reference means.

@BryanKrause the depth would be determined by an optimal balance between clarity and conciseness. Tracing back an idea to when you were in the shower singing a song about yellow submarines is clearly too much. Describing how you empirically discovered during an experiment that your data lies on a curved manifold and thus you wondered whether using a particular differential geometry method would help your model, and how maybe adding this little tweak would confer it some desired property seems just right.

@user3653908 Sure, but... I'm not quite buying the premise that people don't already do that in the circumstance you describe.

@user3653908 Here's an example from a paper I coauthored (Hentschke et al 2017): "Here, we investigated the effects of isoflurane in mouse auditory TC brain slices on evoked and spontaneous neuronal responses, focusing on polysynaptic, propagating network activity (‘bursts’). Our interest in network bursts derives from the importance of the activity of ensembles, rather than of single neurones, in cortical processing.14–16" We tell you concisely what we're going to do and why we're doing it, with reference to 3 papers.

@user3653908 I'm looking at another paper of mine, https://doi.org/10.3389/fnsys.2014.00170 and the logical flow of the introduction starting in the 2nd paragraph is "Here is how people talk about things, here is how this is a problem because of contradictory evidence, here are more reasons this doesn't seem to apply in our special case, here's an alternative framework that we are going to use for the rest of the paper."

My answer is from a mathematics perspective.
I like the analogy of exploring an alien landscape for doing research in mathematics. In this analogy, the ideal mathematical paper reports on having found some astonishing landmark together with useful instructions of how to get there.
Good instructions for how to find a place will look very, very different than the journal of the first explorer to get there. In the latter, you'd have stuff like
I was trying to reach the summit of that mountain, but at some point I found myself separated from the summit by a deep gorge. But when I looked around, I spotted that serene lake between the tree tops. So I climbed back down and tried to make my way to the lake. Then I caught malaria and walked circles for a while hallucinating. After recovering, I made a lucky guess and stumbled upon the lake again, and it really was very beautiful.
But when we are actually dealing with math research, it probably makes much less sense. Reaching the point where you understand an idea well enough to even tell a fellow researcher about it can be hard work. There is "For a few months I thought about whether X could be a good way to attack my Y problem. I don't remember why I ever believed that this might have worked out". There is "one morning I woke up, and under the shower it suddenly all made sense". None of these are particularly helpful for a reader.

Waking up with an insight is pretty common, actually. The brain works even at rest. Sometimes even better at rest. Mathematical "dreams", on the other hand, often have very strange logic.

@Buffy I think Arno's point is not that it's uncommon, but that it's irrelevant or at least unhelpful to a reader trying to understand the paper to know some step was a result of an insightful sleep. Well, maybe it could encourage the reader to develop better sleeping habits.

@BryanKrause, actually, I was "catapulting" the writers idea, not criticizing it. But yes, sleep and exercise are good research habits.

@Arno while I agree with the sentiment expressed by your answer, I am not suggesting that people write how they came up with the solution while they were taking a walk in the park. Identifying the source of inspiration or how one has ideas is a separate discussion altogether. I am suggesting that you describe the motivation behind the different steps you used in your solution and why you thought each of these steps was well suited to solve the particular sub-problem at hand, even if it is just a "X is a method that is applied in vaguely related problems so we investigated further".

@user3653908 My point is that you seem to vastly underestimating how messy the proof process often is. I see explanations of the kind you wish for often enough that it is very plausible to me that most mathematicians include them whenever they had a clear rationale for trying something that they can express easily in words.

@user3653908 "X is a method that is applied in vaguely related problems so we investigated further." The good ML/NLP papers I read usually mentions this part. It's not really a thought process, but a justification as to why that method is suitable. Without the justification, usually it will be harder to be accepted, due to not situating their work in the context of existing work appropriately. Also usually the "failed methods" are also mentioned, but as an aside or in a footnote. But it's still there (usually to avoid questions like "why didn't you try ...?")

@justhalf yes, the "failed methods" is now being included in some papers. Still, I think it should be a much more widespread practice.

@user3653908 I still think you're underestimating the scale of the question, here. I've written <10 page papers for which the those sections would stretch into hundreds of pages.

@user3482749 I agree that it could become too much. Common sense still applies here. So it would be a balance between maximum clarity and conciseness. Is this difficult? Most definitely. Could it make some papers way more accessible and let researchers learn with each other different strategies of attacking a problem (research meta-learning)? I believe so.

I can speak to why this is the case in mathematics; I suspect this bled over into the physical sciences as well but that's just a hunch.
When I was an undergrad I tried to read Rudin's Principles of Mathematical
Analysis and at first I was totally perplexed -- seemingly out of the blue, he'd pull out the value of what some constant had to be in order to make the theorem go.
I felt very stupid and asked my dad about this.
He explained to me that this style of exposition is deeply ingrained in the culture of mathematics, going at least back to Carl Friedrich Gauss, who famously said, "A good building should not show its scaffolding when completed."
He then gave me some strategies for reading, understanding, and writing proofs, and part of that involves reading and writing in an order other than the order in which it will be presented.
None of my professors gave me or anyone this advice, we were just expected to figure it out or fail.
Looking back, I think this was basically a form of gatekeeping.
There have been mathematical works that are presented in a different style, with much more background and exposition.
I think one of the most notable examples is Melissa E O'Neil's work on random number generation.
The PCG RNG paper did not appear in a peer-reviewed journal for some time because the ones she sent it to objected to the style of her writing, which presents much more of her thought process than would be customary for a math paper.
You can read more about the history of the PCG paper from O'Neill herself here.
The algorithm has since been included in several major software packages including numpy.
Personally, I find the PCG paper to be a great read, and I think the fact that she had such a hard time publishing it is a damning indictment of the culture of mathematics.
So, to summarize, the reason more papers aren't written in this style is because (1) it's an ingrained habit that comes down to the present day through Gauss and other early 19th century mathematicians, and now (2) the consequences of trying to change the culture around this include doing a lot of unrewarding work and getting rejected repeatedly.

This answers my question. I was hoping to start a good debate on how things could be better, but it seems most are focusing too much on how involved the process is rather than how something would be vastly better than parachute definitions and the likes. Hopefully, this discussion will motivate more people to slowly abandon this terse style.

Robert Ghrist's book Elementary Applied Topology is an interesting example that very much breaks away from the traditional writing style, but that was self-published. A lot of people have also taken to blogging for the things that can't quite go in a refereed publication but which are still worth writing about. So it seems that the route is to seek other venues rather than to change the way traditional academic publishing works.

@user3653908: " Hopefully, this discussion will motivate more people to slowly abandon this terse style." It certainly will. Look at the number of upvotes the question received.

In papers from my field (chemistry/materials science), it is actually quite common to do exactly what you suggest. However, you will not find anything of that in the typical methods/experimental sections because those are reserved for a description of what was done. The why is part of the discussion part of papers where authors explain what the underlying reasons are for their results, but also should justify their methodology if it is not obvious. Sometimes, this involves giving short mentions of attempts that failed to give the desired outcome.

I am not sure that we really have access to our actual thought processes behind our research, just the post-hoc internal explanation that we remember.  It would require us to actively analyse and record our thought processes as we go along and who has the time for that.  This may just be me, but if I am working on something my ideas and assumptions change and evolve as I perform experiments (partly because the next experiment are shaped by the results of the previous ones).  I don't think I am able to roll back to the version of me at the start of the project and actually evaluate what I thought at the time.  I think it is a mild case of the incommensurability of Thomas Kuhn's paradigms (but on a smaller scale).
Generally when I write a paper, I try to create a logical progression of ideas to explain what I found out during the course of the study.  This is very rarely the same as the historical progression of ideas, with all of its blind alleys and "my brain hurts" moments (again that may be just me).
We are all prone to post-hoc rationalisation of events, it is human nature.  I strongly recommend the film Rashomon to anybody that hasn't already seen it.   I doubt the Rashomon effect named after it only applies to recollection of external events, or to situations involving personal gain/loss.  I am not convinced that we are really objectively that aware of our thought processes and motivations.

Interesting points. Thanks.

When you read a fiction book, you never have the author's thought process on how he designed the book. You may have the narrator's thought process in some books, but not the author. You only get the final output.
When you watch a movie, you don't have the thought process of the director included in the movie. You only get the final output. That would be dumb in many cases, as it would distract from the movie.
A research paper is this, the final output of a research process. A finely crafted piece of science whose goal is to convey information to fellow researchers in the most efficient and concise way.
If you want an example of reading about the thought process of some researcher, you can read Birth of a theorem by the mathematician and Field medalist Cedric Villani.

(I’m coming mainly from mathematics, with some crossover experience in theoretical CS.)
Most good academic writing does include a bit of “thought-process” explanation.  The style you describe as “the default way” — writing as though everything “fell out of the blue” — does exist, but it’s not the default in fields I know (though it was more common a generation ago, in some areas of pure maths).  Papers with no motivating explanation at all are seen as unpleasantly dry; it’s not uncommon for referees to ask for better motivating exposition.
But it’s usually just a little: Too much “thought-process” explanation is not as helpful to readers as you seem to expect.  I’ve read papers that tried hard to explain a deep and subtle thought-process, but came across just as impenetrable waffling, and left me wishing the authors had just stuck to the facts.  The main problem with such attempts is described well by Brent Yorgey: Abstraction, intuition, and the “monad tutorial fallacy”
So in sum, good authors are certainly conscious of this aspect, and usually choose to include some thought-process explanation, but not much.  If you think more would be better, then be the change you wish to see in your field, and use a bit more in your own writing.  But be aware of the reasons why most writers use only a little — remember the parable of Chesterton’s fence!

+1 Chesterton's fence is very apposite!  Very relevant to debugging computer programs, but I suspect my students may appreciate the reference as much as I do! ;o)

For many research-level results I have obtained, the thought processes involved hundreds of wrong turns, including useless definitions and wrong proofs, and it would be really silly to include all those. Unfortunately, it is rare to be able to cogently deduce the results without significant trial and error, otherwise it would not be worthy of research, would it?
However, it is true that in many cases one can come up with a fictitious thought process obtained by splicing together the actually useful ideas that led to the final solution. And that ought to be included in any good exposition of the results. Sadly, there are still page limits for many peer-reviewed publication venues, putting a strain on the desire to give a verbose story of how to come up with the results.

Different people follow extremely different thought processes. Thus, it is not, in general, extremely productive to force all people to arrive at a certain conclusion in the same manner. A closely related concept is described quite well by physicist Richard Feynman in this video on youtube(section beginning at 55:01) (from a BBC special "Fun to Imagine").
Consequently, presenting with a focus upon the important conclusion is a strong strategy. If it is necessary, one can subsequently build "walls" around the core idea being presented by describing other attempts to solve the problem that were eventually unsuccessful.

Also from a mathematics perspective.
I was working on a problem for two years now, and, recently, in the process discovered another (in my opinion) nice connection/result. I wrote a paper about that (under review). I also felt it might be helpful for a potential reader, to better evaluate and understand the results, to give away some history and thought processes involved.
However, from a logical point of view, it seemed more natural to me to present it the other way around: first present the discovered result and after that the application to the original problem that brought me there. Nevertheless, I gave a "personal story" in the introduction, I wrote something like this:

Now, maybe it is best if I give a little bit of a personal story about
the results presented here, and that they are, in some sense, the
result of two strands of thought.
[... following approximately one
page of text, describing a collaboration, where the original problem
came from and how it led to the present results...]
As is most often
the case, and to have a clean separation between a more
group-theoretical part and a more automata-theoretical part, the
presentation does not follow the order of discovery in this sense.

After that, I continued to present the results. I hope the reviewers like my "historical account" ;)

Let me give a different perspective, coming from mathematics.  Other answers have already explained the reasons why papers generally don't have so much thought process, so I won't rehash them here.  Instead, let me contend that giving the full thought process has its own merits, and that more examples of this would be a positive thing.
The most prominent example I know of where the full thought process is given is in Grothendieck's Pursuing Stacks.  It is hundreds and hundreds of pages long with endless digressions.  Nevertheless, it has been influential, and even personally, it has taught me quite a lot per page, though I don't work in this field.  In fact, it is by far the most enjoyable mathematical text I have ever tried to read.
Why do I think this is?  Briefly, because I think it models discussing math with others much better than textbooks/papers.  Generally speaking, discussing math with others is important for learning because it allows for the freedom of exploring ideas with another mind.  Textbooks/papers are important because they give you shortest path to various goals and prove a lot of important results, which you can study on your own.  So I see two needs which papers giving the whole thought process may fill:

For people who don't have people they regularly discuss a certain area of math with.
For people looking to just enjoy math, not concerned about publishing papers in a certain field.

Finally, let me add that even from a "practical" standpoint, texts like these may have the potential (apart from the actual ideas they may contain) to expose students to the unconstrained nature of doing research, which at least for certain students may be important to building their own tastes.


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What is the APA7 rule when quoting a translated non-academic speech?
My field is in the humanities, and I need to quote a passage from an audio-visual recorded speech (around 30 words) in French, by a politician. It is for a paper written in English.
I would like to have the precise wording that they used rendered; in other words, I would like to quote it as a direct speech, not as an indirect speech (the precise wording being transcribed by a faithful translation). It seems to me this rationale makes totally sense, since a political speech is about a specific rhetoric, in contrary to scientific pieces.
It is difficult to find informations on this case:

https://www.scribbr.com/apa-examples/speech/

https://apastyle.apa.org/blog/cite-your-own-translations

https://research.wou.edu/c.php?g=1046948&p=7596966

The last link is quite straightforward:

Only quote an indirect source when absolutely necessary, for instance, when the original work is out of print or unavailable, or in a foreign language.

NB: With the APA restrictions, what I plan to do is to quote the passage in the original language, and accompanied (either in a note, or between brackets in the main text) with an English translation.
There is a related question here, but it concerned the quotation of a passage from an academic piece.

Although the related question concerned a quotation from an academic text, the APA guidance does not seem to assume this. Indeed, I would imagine that the rationale "Further, because it is not possible to codify how exact any given translation is, it would be inappropriate to put quotation marks around the translated words; again, your translation is a paraphrase." might be more relevant to speeches possibly using informal or idiomatic expressions. Anyway, your plan seems very reasonable.

I would object to a number of premises here. One, you can't have "the precise wording that they used rendered" when dealing with a translation. I also do not think that political speech is definitionally more precise. In fact, scientific writing is often criticized by being overly precised to the point it makes reading difficult.

For the purposes of citation, there is no difference between an "academic" and "non-academic" work.

@AzorAhai-him- Thank you. I changed to "specific (rhetoric)"

Now I'm not really sure what you mean at all.


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English Language requirments for Masters in Italy?
Obiviously it may differ from university to university but i am about talking about general case which is applicable to majority
IELTS and related certificates are necessary or English Language Proficiency certificate from previous university(Bachelor Degree University) of student also acceptable?

Why would you need any English Language requirement in Italy?

@Buffy - many European universities are teaching in English these days.

@JonCuster, hmmm, serves 'em right for making us learn French, German, and maybe Russian in grad school. HA!

I cannot speak in general, but for the MSc course I teach in (in English) no certifications are mandatory; it is sufficient to submit a written declaration on penalty of perjury (autodichiarazione) that you have at least a B2 level (source).
I imagine it is going to be the same in many other universities, since this kind of declaration is regulated by law in Italy; but I have no direct experience.

So, basically, if you can’t keep up with the English instruction it is on the student. Seems reasonable.


text:
How to cite long term unofficial usage of a non-approved medical device in the results section of a paper?
My research group has developed a novel medical device that is much needed in the area of diagnostic laboratory medicine that we are working in. We are writing a paper on this device and its results in a high impact journal and currently have a small sample of results we are discussing in the paper.
However, one of the authors was able to get this device in full operation in a medical facility in his home country, which is a 3rd world country that does not have strict regulations on such devices. It's been operational for over a year and has produced amazing results with no problems thus far.
Would I cite the medical institution and country it was used at? The medical professionals opinions who made use of it? How do I prove we even did this rigorously?
How can I cite this result in the paper? How can I say "We actually built it and used it on real world medical samples for a long period of time and it worked great" Since that wouldn't exactly be allowed here with all the regulations that exist.
Note that this device is for post processing and does not have any risks of usage or affect the patient in any way so I doubt there is any ethics issue (is there?). It's more like a faster algorithm for diagnosing something that relies on a physical device we built.
This device would fit under FDA Class I medical device or EU IVD certification. But we don't have that yet. (We will soon). Yet in the country it was used we did not have this prior to using it.
Thank you

Why are there regulations on such devices in your country if it does not entail a risk to the patient? You may want to consult your ethics board, in any case.

@CaptainEmacs If I were to give an analogy, imagine someone builds some little attachment device that goes onto an MRI machine and somehow allows the imaging to be faster and more accurate than normal (that's not what we did just a random analogy). Would such a device need approval to be used in the USA/Europe? That's basically what we are dealing with here and so far we've only been able to implement it in the lab and also in a medical institution in the authors home country.

I don't understand why you wouldn't just describe what you did and where. A citation is to reference some other published work. If your current paper is going to be the first publication and your coauthor has done this work themselves, then that work need merely to be described in sufficient detail in the current work.

@BryanKrause How exactly would we write up results for the actual implementation that was done in the other country? For example prior to this happening, our results section was simply 12 test cases done in the lab to show that it works. But now we are talking about potentially having used it over 20,000 times for real patients and judged by real doctors and getting excellent results. I don't know how to approach writing that in the results section.

@DanielHatton No. It's something general that is ideally going to be used everywhere but might also be scrapped for something even better we are working on.

@Snailshell That's a domain-specific question we can't answer.

@Snailshell Seems to me like having 20,000 results is a lot better than having 12. I'd build your whole paper around it. The pilot of 12 seems fairly irrelevant at that point unless you need to make a point about translation to a different environment. There isn't any special academia-wide answer here, it sounds like you're just asking "how do I write a paper?" Presumably people in your field write papers about more than 12 participants, how do they do it? It's also super common for interventions to be first trialed in a softer regulatory environment.

@BryanKrause Thank you that helps.

Why "cite"? Just describe this in the text.


text:
How does a community or field, generally accept a paper in academia?
I'll just rephrase what I had said in the comments, as I believe it summaries the question better, and to whoever downvoted ( assuming because of how "complicated" what I said was ) I hope this suffices:
There exists proof methods, particularly so whatever a claim may be, an undeniable truth arises from a well executed proof, and when the proof methods are carried out, you may arrive at a result that satisfies the claim therefore, but the problem in question may have another given in the claim that is unknown, or unapparent. How would one know there isn't some unknown or unapparent fact also applied to the claim. How would this effect a paper being published, and if this is fact, what would this say about how the community treats papers generally?

Sorry, I have no idea what you are asking. Maybe an example would help?

Ok, I will state that in my question.

I think it will be better if you are more precise; if you narrow down your question to your particular field of interest.

I would have done that, but since its academia, and not say, mathematics, I don't want to get banned off or something along those lines. To be fair, I did provide an example.

I did provide an example --- A more carefully written example would help. For example, "the curvature of the moon is anything other than say, a line" seems to suggest that one answer to the the question "what is the curvature of the moon" is "a line", which is a category mistake. Probably you mean "the curvature of the moon is anything other than the curvature of a line", which could be better expressed as "the curvature of the moon is not zero". This is only one of many awkward or not-quite-correct wordings in what you've written that collectively makes your message extremely opaque.

Your right, Dave L Renfro. I noticed that mistake before, and failed to edit, as I figured it would be overlooked both ways, and figured it may not necessarily affect what the question was in the first place, anyways. But yes. If you could do anything to help me understand what more, is exactly hard to understand, I'll edit them. Starting with that one.

If I understand correctly, a more concrete way to put the question may be: "How does the community deal with a theory that may be correct but cannot be proven?" And a good example may be the situational in physics particle theory -- there are tons of papers that posit ideas (e.g., higgsino models in the MSSM); these models might be true, and certainly would address many open questions if so, but there is not yet proof either way.

But there exists proof methods, and when the proof methods are carried out, you may arrive at a result that satisfies the claim, but the problem in question may have another given in the claim that is unknown, or unapparent. You see the problem with what you said, is it would surely imply all papers are of such nature. Is this truly the case? How would one know there isn't some unknown or unapparent fact also applied to the claim. With reference to the example, if light was unknown to us, how would we know to factor it in with the curvature of the moon.

I appreciate that you've taken some effort to clarify the question, but it's still not clear what sort of answers you are looking for, unfortunately. Some of the "big questions" you raise would require a textbook about metaphysics or epistomology to answer (which is not our role). If there is a more immediate question about how the world of research handles uncertainty "in real life", it is not clear to me.

Then, could you at least say, if true, the way the research community handles such things, can only be described by epistemology, and no explanation could be given otherwise, such that it would fully go beyond the bounds of just simply keeping the bounds of the answer within academia and that alone; if so, can you please guarantee, or otherwise, answer that question. Or at the very least, simplify it ( if possible ) to bounds academia will allow.

These are all questions not answered to me, just "no, this is not relevant" without explanation, which is the sole most crucial thing in a response if you ask me. Either that, or I can provide explicit evidence of events where often, the answer in question, is in truth, blatantly ignoring points made in the question so as to act in a condescending manner.

I think its reasonable to be able to explain that when you research for a paper, with the purpose or great likelihood someone will use it, to apply it. And if you don't even know when its applicable... and if your literally the person researching, your telling me you don't care? To be frank, its becoming increasingly more cryptic when you do care and when you don't, because you certainly care enough to prove everything, just to a point where you can't can't explain everything.

And if you can't explain everything, this clearly shows you don't even care about thinking about that. Instead, when I do ask for a question on this site, I instead get an answer that completely ignores the forefront of the question that I had asked, the actual meat of the question.

I am trying to understand your question: You are worried that there are unknowns that might invalidate a theory, and you want to know what science does about that. Unknowns are, by definition, not known, which limits what you can do about them. So what we do about that is that we never accept a theory as true. We consider some theories more plausible than others on various good and less good grounds.

I interpret your question as follows. Is it accurate? "When researchers study a system, one method is to build a mathematical model for it and prove that it has various properties. Then the real system should have those properties as well. But a model is never 100% accurate because of extra assumptions and simplifications, which might even be implicit and unnoticed, so the results don't match reality. How do researchers take this into account when writing and reviewing papers?"

Maarten Buis, thank you.

IIkka Torma, I would say that is an example. I don't suppose that example could not be generalizable ( in this case, your describing a system, say a physical system ). So under those terms, I would say you are correct, and if you could apply that to academia ( the question itself ) and report what written or unwritten rules permit the community to achieve conclusions by working around such uncertainty, I would consider that a clear, and great answer.

@cag51: "How does the community deal with a theory that may be correct but cannot be proven?" --- This issue arose in a physics Ph.D. defense I attended in February 1980. (I have a copy of the department notice for it—why I know the date—that was advertised on the math/physics department bulletin boards for anyone interested to attend.) A complaint that was raised (by this person, I believe) that the theory as yet did not include interacting particles, and thus was not even theoretically experimentally verifiable. (continued)

Dave L Renfro, so far, I would consider this the best answer, thanks a lot.

I'll restrict this answer to the field(s) of pure mathematics and similar things. For other fields the standard will be very different, say philosophy or psychology.
First, math has a clear standard of proof and truth. To be true, in the modern conception held by most mathematicians, something has to be derivable from a set of axioms using well determined logical rules. But the chain of proof can be very long, depending on other things "held" to be true and previously proven. Some of the chains began in ancient times. Many others about a hundred or so years ago when axiomatic mathematics came to the fore.
Second, people make mistakes. The chains of proof can be long. They can also be very twisty and when there are many levels of abstraction in a proof it isn't terribly difficult to make an error. Sometimes a mistake is made because someone has an "insight" into a problem that is very subtly wrong, but seems right. This might cause them to gloss over difficulties. Others with the same background might make the same error. It might be effectively impossible to check the complete chain of proof, due to its length.
Moreover, prior to the axiomatization of math, the standard was quite different. Sometimes verification depended to some extend on applications of theory. Some of that was re-established axiomatically, but there is a huge body of "knowledge".
Third, the work of mathematicians, if sufficiently important, is checked by other mathematicians, who are, themselves, skilled, but not perfect. Not all errors are caught in the publication review process, even though papers are normally reviewed by a few independent mathematicians with skill in the particular field. Not all errors slip through, but a few do. And generally, the "checkers" are happy to show their work, explaining why they accept (or not) the proofs in a given paper.
Fourth, we normally trust the experts, but also generally maintain a wee bit of skepticism generally, knowing the history of long lived errors.
So, for a direct answer to the question, if a paper has passed review it is likely, but not necessarily, accepted by the mob. But "completely accept" is possible for simple results that have stood the test of time. Complex results and complex arguments, not so much. I once found an error by an important mathematician that had been in place for more than 50 years. It was buried deep in a complex proof but was, in its nature, somewhat elementary.
The wee bit of skepticism often leads professors to have their doctoral students examine the reasoning in old papers (just as I was) to verify them. The verification doesn't add much to the mathematics unless and error is found, but it can add immensely to the insight and understanding of the student.
So, the answer of Anonymous Physicist is not incorrect at all, though it lacks detail.
"Trust but verify" is the standard.
What you do about it is verify in any case that is vitally important to you. But generally trust what other mathematicians, perhaps with more experience, have concluded.

In math there are four possible outcomes for a statement. True, if it can be proven (derived from axioms). False, if a counterexample can be found. Unknown if we have neither a proof nor a contradiction. Unknowable, since axiom systems can't be both complete and consistent.

Caveat. I have a certain philosophy of mathematics that a few others don't hold. My philosophy leads me to think in a certain way and do mathematics in a certain way. It is pretty widely held, but not universal. Others with a different outlook might just pop in with a rather different answer.

For what its worth ( lot of dislikes on my question ), this answer appropriately answered my question. Also, I would like to say, that maybe this would be something easy to think about for most people- granted, they actually were studying academia, and not just someone like me- who are hoping to at a later point i.e not people who are ignorant to the subject itself. I believe it would have been hard to answer otherwise, hence the relevancy of the question.

Somehow, at the end of it all, I did manage an answer that satisfied me entirely, and I'm not sure what that is to say about the ambiguity or general unclearness of my question, that many seem to say without actual reasoning. Thanks again, Buffy.

I think your question is:

How do academics become completely sure that a paper is correct?

And the answer is that normally they do not.

Let us avoid arguments in the comments. Mike: I understand this answer may not be what you're looking for, but other answers may come.