Getting a paper accepted

I work in academia and have over 70 papers published. I Agree with most ideas in the article. Another dimension not covered is what I called “author engineering”. Many times it is very difficult to “get into” a new field if you don’t have an author known by the editors. I work in applied math (very transversal) and happen to me often to be rejected because “I don’t belong to the area”. PhD students usually don’t suffer from this as the supervisor is already a member of the community. But if not, try to bring a collaborator that is known in the area. This is usually done in conferences by chatting with people.

Academics seem to have this fixation on "ideas":

> And it’s not just a pace thing, there’s a threshold of clarity that divides learned nothing from got at least one new idea.

But these days, ideas are quite cheap: in my experience, most researchers have more ideas than students to work on them. Many papers can fit their "core idea" in a tweet or two, and in many cases someone has already tweeted the idea in one form or another. Some ideas are better than others, but there's a lot of "reasonable" ideas out there.

Any of these ideas can be a paper, but what makes it science can't just be the fact that it was communicated clearly. It wouldn't be science unless you perform experiments (that accurately implement the "idea") and faithfully report the results. (Reviewers may add an additional constraint: that the results must look "good".)

So what does science have to do with reviewers' fixation on clarity and presentation? I claim: absolutely nothing. You can pretty much say whatever you want as long as it sounds reasonable and is communicated clearly (and of course the results look good). Even if the over-worked PhD student screws up the evaluation script a bit and the results are in their favor (oops!), the reviewers are not going to notice so long as the ideas are presented clearly.

Clear communication is important, but science cannot just be communicating ideas.

I think the professional sciences has, for a long time, been a social game of building ones career but it does feel like it's metastasized into something that's swallowed academia.

From the first article in the series [0]:

> Insiders ... understand that a research paper serves ... in increasing importance ... Currency, An advertisement, Brand marketing ... in contrast to what outsiders .. believe, which is ... to share a novel discovery with the world in a detailed report.

I can believe it's absolutely true. And yikes.

Other than the brutal contempt, TFA looks like pretty good advice.

[0] https://maxwellforbes.com/posts/your-paper-is-an-ad/

I am not the original author, but I posted this since it mirrors some experiences I have had in my PhD so far submitting papers. This kind of tweaking in paper and writing even happens when writing the first draft or sometimes even in the conception of the research idea or how to go about the implementation and experimentation.

There is a half-joke in our lab that the more times a paper is rejected, the bigger or more praised it will be once it's accepted. This simply alludes to the fact that many times reviewers can be bothered with seeing value in certain ideas or topics in a field unless it is "novel" or the paper is written in a way that is geared towards them, rather than being relegated to "just engineering effort" (this is my biased experience). However, tailoring and submitting certain ideas/papers to venues that value the specific work is the best way I have found to work around this (but even then it takes some time to really understand which conferences value which style of work, even if it appears they value it).

I do think there is some saving grace in the section the author writes about "The Science Thing Was Improved," implying that these changes in the paper make the paper better and easier to read. I do agree very much with this; many times, people have bad figures, poor tables or charts, bad captions, etc., that make things harder to understand or outright misleading. But I only agree with the author to a certain extent. Rather, I think that there should also be changes made on the other side, the side of the reviewer or venue, to provide high-quality reviews and assessments of papers. But I think this is a bit outside the scope of what the author talks about in their post.

Ot was a fun read, but, how do you know these changes made your paper better? Your assumption is that reviewers approach the reviewing process with the same knowledge and goals, or are quite objective, but that's mot the case in all my publication history. So how can you prove causal effects with 1 sample?

Another strategy is to write a descent paper and submit it somewhere good. If it’s accepted, great. If it’s rejected and the comments make sense, improve the paper based on the comments before resubmitting somewhere else. Otherwise simply resubmit somewhere else.

I think it’s equally likely that the second version just got a different set of reviewers who randomly liked it more, and the revisions didn’t make a big difference. Having submitted lots of papers to conferences like this I basically think of the reviewer ratings as noise.

There is saying in english that people "eat with their eyes".

When it comes to papers, I always reminded myself and others that people also _read_ with their eyes.

It is easy to be cynical about this (with some justification!), but if the findings are more clearly and quickly communicated by a pretty-looking paper, then the paper has objectively improved.

Thanks for this post. As someone writing an open-source book (without an editor to help), I find some takeaways very helpful.

But I think your most significant change was changing the "what" to "why".

Reading the original, we can see that most sentences start with "we did..." "we did..." and my impression as a reader was, "Okay, but how is this important?" In the second one, the "what" is only in the first part of the sentence, to name things (which gives a sense of novelty), and then only "whys" come after it.

"Whys" > "Whats" also applies to good code comments (and why LLM's code sometimes sucks). I can easily know "what" the code does, but often, I want to know "why" it is there.

Another Machiavellian thing I have seen in the literature related to "Science 2" is where ML benchmarks or test cases only become accepted when they show that a lot of people's models are working. ;-)

Interesting tips, but it also depends on the field.

If you're submitting to a control theory journal, you better have some novel theorems with rigorous mathematical proofs in that "rest of the paper" part. That's a little nontrivial.

This exact advice applies to resume writing!

I'm a bit afraid that some people will read this article or skim it and say "The fact that you have to do all of this 'branding' is just further proof that science is riddled with irredeemable incentive issues." However, this isn't the author's point. In fact, early the the post, the author writes:

>The tweaks that get the paper accepted—unexpectedly, happily—also improve the actual science contribution. >The main point is that your paper’s value should be obvious, not that is must be enormous.

This is slightly oversimplified, but from the outside, science may look like researchers are constantly publishing papers sort of for the sake of it. However, the papers are the codified ways in which we attempt to influence the thinking of other researchers. All of us who engage in scientific research aim to be on the literal cutting edge of the research conversation. Therefore it's imperative to communicate how our work can be valuable to specific readers.

Let's take a look at the two abstracts:

  (Version 1, Rejected): Given two distinct stimuli, humans can compare and contrast them using natural language. The comparative language that arises is grounded in structural commonalities of the subjects. We study the task of generating comparative language in a visual setting, where two images provide the context for the description. This setting offers a new approach for aiding humans in fine grained recognition, where a model explains the semantics of a visual space by describing the difference between two stimuli. We collect a dataset of paragraphs comparing pairs of bird photographs, proposing a sampling algorithm that leverages both taxonomic and visual metrics of similarity. We present a novel model architecture for generating comparative language given two images as input, and validate its performance both on automatic metrics and visa human comprehension.

Here, the first two sentences a) make a really obvious claim and could equally be at home in a philosophy journal, a linguistic journal, a cognitive science journal, a psychology journal, a neuroscience journal, even something about optometry. Moreover, some readers may look at this abstract and think "well, that's nice, but I'm not sure I need to read this."

  (Version 2, Accepted): We introduce the new Birds-to-Words dataset of 41k sentences describing fine-grained differences between photographs of birds. The language collected is highly detailed, while remaining understandable to the everyday observer (e.g., “heart-shaped face,” “squat body”). Paragraph-length descriptions naturally adapt to varying levels of taxonomic and visual distance—drawn from a novel stratified sampling approach—with the appropriate level of detail. We propose a new model called Neural Naturalist that uses a joint image encoding and comparative module to generate comparative language, and evaluate the results with humans who must use the descriptions to distinguish real images. Our results indicate promising potential for neural models to explain differences in visual embedding space using natural language, as well as a concrete path for machine learning to aid citizen scientists in their effort to preserve biodiversity.

Compared to V1, the V2 abstract does a much better job of communicating a) how this project might be valuable to people who want to understand and use neural-network models "to explain differences in visual embedding space using natural language." Or to put it another way, if you want to understand this, it's in your interest to read the paper!

"Gaming the research game is not Science." Unknown

Oh dear... a monkey has escaped from the circus and is telling us the truth about what's going on inside it.

> "The primary objects of modern science are research papers. Research papers are acts of communication. Few people will actually download and use our dataset. Nobody will download and use our model—they can’t, it’s locked inside Google’s proprietary stack."

The author is confusing the concept of 'science as a pursuit that will earn me enough money and prestige to live a nice life' - in which, I'd say, we can replace 'science' with 'religion' and go back to the 1300s or so - with science as the practice of observation, experiment and mathematical theory with the goal of gaining some understanding of the marvelously wonderful universe we exist in.

Yes, the academic system has been grotesquely corrupted by Bayh-Dole, yes, the academic system is internal blood sport politics for a limited number of posts, yes, it's all collapsing under the weight of corporate corruption and a degenerate ruling class - but so what, science doesn't care. It can all go dormant for 100 years, it has before, hasn't it? 125 years ago you had to learn to read German to be up on modern scientific developments.

Wake up - nature doesn't care about the academic system, and science isn't reliant on some decrepit corrupt priesthood.

P.S. Practically speaking, new graduate students should all be required to read Machiavelli as an intro to their new life.

I have a problem with this. In the old days, people did research for the sake of research, and mostly out of Europe came the greatest scientific works we have seen. I did my PhD in the US, and it is very unfortunate that "gaming" publications and focusing on "grants" is the meat of research. Before I get criticized, I was part of this process at a top 10 university and I am a proud American. It is because of this pride that I must show tough love. I chose to move away from academia without a postdoc because I hated it. I wanted to do research and contribute to work that pushes my field forward. Most (90% of those I met, and I dare say 99% of international students) only wanted a PhD for selfish reasons (entry to US market, salary bump, changing fields, access to RnD jobs, etc). Perhaps I am naive, but I wish more people did research for the sake of research. The only Clay prize went to a Russian who hated academia. Perhaps there is some truth in the fact the immortals in science are not those churning conference papers, but those laying seeds a la Laplace, Einstein, etc. I want to see more of those, because this is what will move the field forward. It is not manipulating metrics to improve a neural network for one use case, while knowing (and not sharing) it fails in every other instance. This is my second beef with research. When something is tried but does not work, it is not shared. Someone else will try and fail, and this build up will overall slow everyone down. I wish we were more accepting of failed trials, and of not knowing the answer (sharing results without the theory is OKAY. It is OKAY if someone else comes up with it using your results. Having spent many years in a PhD, I can confirm the vast majority unfortunately do not share my point of view. And I hope I do not come across as bitter, it frankly makes me sad.

...and that is how you start a career in Marketing...