Comment by kkylin | Hacker News Reader

Comment by kkylin | Hacker News Reader

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I'm an academic (applied math) and want to respond to this: academic papers are the way they are for lots of reasons, many of which (not so good) have been mentioned on HN. There are a couple that I do not see very often however:

(1) Many academics aren't aware non-academics read their papers at all: we work with other academics, go to conferences with other academics, and on the rare occasions we hear from readers, it's from other academics. Big exception: in some fields academia and industry have much more interaction, biomedical research (the subject of the linked article) being one of them. Extracting knowledge from that literature has a large number of practical and economic implications.

(2) There seems to be a perception that published papers are a repository of established or state-of-the-art knowledge. Perhaps they were meant to be that way, and perhaps more of them should be. But for many journals in many fields, publications are a form of moderated discussion. Reconstructing the state of knowledge from snippets of conversation is always going to be hard.

What can help make the literature more accessible? some of the forces are structural, some are due to current limitations of technology. But one thing that can help if you find the results of a paper interesting and are able to track down the authors, write them. People like hearing their work is noticed, and they like talking to people about things they're interested in.

Another is to make constructive suggestions (or even pitch in to improve code where it's open source & available). Between teaching, advising, committee work, etc (not to mention family), most of us have to prioritize, and as much as I'd like to clean up old code for release in the hopes someone finds it useful, it isn't going to get my grad students out the door with a degree or a job -- I'm generally spending more time on their research problems these days than my own. But if I know there's interest / use I might prioritize time a little differently.

> What can help make the literature more accessible?

Review articles, sometimes called surveys.

I've always thought that new PhDs would be excellent authors for those, having digested lots literature for their dissertation.

> Review articles, sometimes called surveys.

Is this field specific? I have read survey articles in math and biology, and was told by some of my profs that they use these articles as an introduction to a new field.

A quick Google search seems to show these exist in CS (along with tutorial papers), physics, and chemistry but I'm having a little difficulty finding statistics survey papers (survey methods come up instead).

Is the problem that there aren't enough of them or they are behind paywalls?

For Stats, check out https://www.annualreviews.org/journal/statistics

Please suggest others if you find them.

Annual Reviews has a bunch of journals for surveys of various fields. Most of them are paywalled, but there's ways around that.

Also, I believe there is a hierarchy that goes something like: academic papers -> review articles -> specialized books -> text books.

The text changes to fit the audience, and the knowledge becomes more accepted (and or fundamental) further down the line.

> I've always thought that new PhDs

A well written PhD or MSc thesis is often the best way into a new field, ime. If the committee is good on this aspect they'll insist you've put enough detail in for someone to follow along mostly self contained.

There's very little academic kudos survey articles or other educational articles (as another commentator mentioned, PhD theses are often the best place to go for those types of summary). I'm not sure how one changes that. I remember thinking during my PhD how helpful it would be to have more survey type articles - especially for the subfields adjacent to one's own - you want to know if there are useful connections to your work but you don't want to delve through dozens of extremely dense papers.

You hit the nail on the head. Will put some of that in the appendix of the post!

> What can help make the literature more accessible?

Publishing code and data associated with figures. A significant amount of the confusion about the literature comes from the difference between the documentation laid out in a paper and the reality of the actual implementation. Plus any peer review of the actual code used is checking the reality of the paper, rather than a description of what we think reality is.

Making this part of the publication process is vital, because there is such a high barrier to actually requesting this information later.

First, let me recognize the openness with which you're approaching the problem, and that you've acknowledged there is a problem. My academic background is also applied math, and I believe there is a missing viewpoint that I've been struggling to articulate to colleagues for several years. I'll take your thoughts as an opportunity to try and better understand this view / feeling. If it's alright with you, I'll limit the discussion to applied math, because it's what I know best and where I think the viewpoint is most pertinent.

Full disclosure: your username is very easy to Google, and I should tell you we work on closely related topics. My opinion is shaped in part by several of the papers you've cited. I'm happy to disclose my identity and continue the conversation in private.

You wrote: "(1) Many academics aren't aware non-academics read their papers at all"

What, then, is the point of applied mathematics? Please know that I don't mean that in a dismissive way. I think there are a few reasonable answers. Chief among them is the belief that exploratory research is important in its own right and does not need to have an immediate non-academic use, as embodied by this quote of Hadamard:

“Practical application is found by not looking for it, and one can say that the whole progress of civilization rests on that principle.”

I believe the above quote as far as it concerns mathematics. But how do we get from there to the applied part? I have an internal dissonance about this that goes deeper than just semantics. Before I started my PhD, I had some vague belief that after writing up some research with an algorithm in it, you'd put it on the arxiv, and from there someone might one day need something like that, code it up, and use it. If I could put in a basic working implementation that was even better.

All the evidence I've seen so far tells me this is not so. The truth is no one is going to take the time to code up your algorithm, because no one has dozens of hours to spend understanding your paper, developing an algorithm suitable for an industrial problem, often just to get improvement on a niche subset of cases. I've been wondering how to estimate the number of algorithms described on the arxiv that are ever implemented and used in a non-academic setting -- my bet is (outside of ML), less than 1%.

I've heard many times that sophisticated higher-order methods for PDEs (finite element / volume, Galerkin, ...) are used in aeronautics, to determine the wind shape over an airplane wing. I've found out from talking to people in the industry at companies like Bombardier that for the most part they do second order finite difference like the rest of us. Why? Because you can code it up in an afternoon, whereas writing the more sophisticated methods can take weeks or months. As academics, we think that the theoretical work is the really hard part, and we neglect the human cost of writing and maintaining algorithms. We have it backwards: academics are (relatively) cheap; code (and changing code) is expensive. (Of course, I make these comments assuming a certain scale. We can come back to this.)

I think the fundamental issue is that I know few applied mathematicians who start with a problem and seek out a solution. Most often, you finish your (applied) math PhD armed with some machinery. If you want to get a professorship and you've done well, you typically turn the crank of your particular machine better and faster than most. In applied math we can say our model is motivated by some problem in the sciences/economics/whatever, but in my experience that just lets us erect a straw person (create a problem) and tear it down (solve the problem) using the machinery that only we have mastered. Just because a problem is hard doesn't make it important.

What to do, then? How do you work on "consequential" problems?

To be pithy about it, I've found it useful to think in terms of $ rather than h-index. In many cases, a consequential problem is one that, if you solve, you can monetize. You could frame this as asking what kind of mathematics could enable new technologies. In my experience it is very difficult to write down a mathematical question that, if answered, can lead to new technology. But if you manage to find such a question -- and it is possible -- it can be a goldmine.

I have more to say -- especially about how mathematicians need to get a reality check on the importance of hardware and its relevance in stochastic algorithms research -- but this is long enough as it is, and I don't want to just be a crazy person rambling in the corner. I'd be very curious to hear your thoughts.

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