Science in the age of selfies (2016) | Hacker News Reader

Science in the age of selfies (2016)

pnas.org

> We spend much of our time taking “professional selfies.” In fact, many of us spend more time announcing ideas than formulating them

This is closest to the truth than anything. Lengthy CVs attract public science funding which makes the same CVs lengthier which attracts more funding and on and on. Hordes of people have attracted fame and notoriety by academic SEOing. It's a gamed system, and it doesn't feel great participating in it (but you never say that, you have to be perpetually 'excited' on twitter). Basically Goodhart's Law on massive scale.

Perhaps the solution is indeed to build an AI because our science has become so loud and noisy that we 've become deaf.

What a negative comment, and i find it hard to believe accurate. Yes there is such phenomena but ending with a "perhaps let's just pack up and deliver the problem somewhere else" is outrageous and further confirmation that the poster is out of ideas so let's apply "AI".

Scientific methods and the institutions in charge of applying resources towards it are not perfect but our life has seen incredible improvements in the last few years. Hell, we just came out of a pandemic with a brand new type of vaccine! I just recently read about the rotavirus vaccine changing gastroentritis at nation's levels. A vaccine that appeared in 2006, so quite after I was born and I am a 90s guy. How can one say science is selfies? I refuse to be put down by such a world view.

> Scientific methods and the institutions in charge of applying resources towards it are not perfect but our life has seen incredible improvements in the last few years

Private research is doing just fine, it is public research we are talking about here. The examples you mentioned were developed by private companies.

... building on decades of prior research, much of it public (e.g. https://www.nature.com/articles/d41586-021-02483-w)

You might unintentionally be proving the point of the person you're responding to: that mRNA vaccine you mention has been filled with examples of the problems with modern academic science. One of the main developers of mRNA vaccines was demoted at Penn because she tried and failed to get grant funding for how to develop them, and was told to stop working on it because "it wouldn't work" and wasn't bringing in money to the university. There were other stories by others involved in developing SARS-CoV-2 mRNA vaccines that their papers on the topic were repeatedly rejected from journals before the pandemic.

It would be a mistake to attribute mRNA vaccines to any one person or even small group of persons but it's not clear to me that it's a good example of how the standard academic system is working.

Tangentially related, but it reminds me of a quote by Nassim Taleb: "The curse of modernity is that we are increasingly populated by a class of people who are better at explaining than understanding, or better at explaining than doing." (from Skin in the Game)

I think he said this with regard to school system (teachers and teaching). Taleb's bitterness often feels tiring and pompously unfair (albeit also funny), but I have felt something similar after years of working in journalism.

“Clumsy writing begets clumsy thought, which begets even clumsier writing. The only way out is for the rest of us to look back to a time when authors had more to say than, “I'm a writer!” — when the novel wasn't just a three-hundred page caption for the photograph on the inside jacket.” - B.R. Myers, A Reader’s Manifesto

Maybe one difference today is that academia is being increasingly flooded by careerists.

In 1950s, most people did not even go to college, let alone grad school. The people who went into science in 1950s were not in it to “get a job”.

These days, many students see grad school as the simple next step after undergrad, because the job opportunities are not good for many new grads (eg biology bachelors).

We therefore see a huge influx of phds and academics, but they are entering academia for different reasons (careerists) than when people did it in 1950s.

I wouldn't say that science is flooded by careerists, it's kind of the opposite - the scientific system demands you to be this way.

There are exactly 0 jobs for undergrad BSc in most countries, especially if you want any kind of liveable salary and not be a pipetting robot.

Most interesting jobs require at least a PhD, and these requirements are usually firm - there is a glut of applicants, employers can save some work by deleting those who don't fulfill the requirements and still get good people. You can't get postdocs without a PhD (from my experience, in >95% of cases), and a postdoc is the 'prescribed' way towards a Prof. If you are trying to come in mid-career from a non-academic background, you don't have the papers, you don't have the grants, you're a hiring risk.

The system itself makes careerists, it only very rarely allows outsiders coming in.

>You can't get postdocs without a PhD

My understanding of a postdoc position is that it's "post" (after) "doc" (doctorate/phd)

I'm not exactly sure what you mean by this, could you explain a bit?

Yes, that's what it means... but I have on very rare occasion seen people with (money and) industry experience work in a an academic lab with other post docs bringing in the same meager stipend. It's doubtful that they are working towards a professorship, although a PhD isn't actually needed for that either. I've seen a few with only BS or MS degrees working as teaching professors.

yes, after finishing PhD, people who want to become professor should get a postdoc position as soon as they can, and finish work as more as they can during it...

Yes, it's postdoctorate, but it's the only academic job for people in their ~30s. It's the equivalent of an early/mid-career programmer. There's just no other title so they're all called postdocs, even if (rarely) they don't have a PhD.

Re: mid-career, no papers -- does this effectively result in ageism inside the academia? Widespread, but poorly hidden?

It's even worse than that.

You'll find tons of grants--and even jobs--that are time-gated: you must be within k years of your PhD, where k is pretty small (1,2, maybe 5). Annoyingly, these apply to some faculty-level things (e.g., ESI status for NIH grants), so if you start on the back foot, it's hard to catch up.

This is bad for a lot of reasons. It's discriminatory (and arguably illegal, since it must have a "disparate impact" on people over 40). It also shapes the sort of research people do. It encourages trainees to work on established projects in established labs, which promotes orthdoxy and thwarts the idea of training people for careers as independent investigators. It rewards choosing techniques that are "safe" and fast-moving.

> You'll find tons of grants--and even jobs--that are time-gated: you must be within k years of your PhD, where k is pretty small (1, 2, maybe 5).

I finished my PhD in 2020. The research job market was terrible back then, and I'm getting close to the 2 year mark where I'll age out of a lot of opportunities. These rules are going to prevent a lot of people from getting a job in science, and there's nothing they can do about it.

Fortunately, in the case of many jobs, I think the time-since-PhD requirement is often boilerplate that is not enforced.

In many places the time-since-PhD is also relevant to opportunity, so if you are 5 years post-PhD but you were unemployed for 2 years or outside academia, you count as 3 years post-PhD.

Yes, there is lots of ageism. If you don't have a Professorship in your 40s you're out, you'll rarely get a position. I've seen rare cases, but then you'd have to be really successful in your non-academic job - for example, upper levels of NGOs get recruited for highly visible professorships around NGOs, or business people get recruited for 'start up' professorships. These positions are usually 'handcrafted' for the business-person. But there's nothing below that, people won't jump into a mid-level academic career, those positions are full of 'regular' academics.

Not just publications, but other reasons too - someone in their 40s has family and is not willing to work brutal hours and weekends required in many research labs, which someone in their 20s is more likely to accept. Of course it's worse if you're a mother.

The article makes some serious and cogent observations, but itself ignores an important possibility. Post 1965 we have a number of new tools which we still don’t really understand, and are using them in domains previously under explored or even unknown. It’s quite possible that we are in another era of “butterfly collecting” to be followed by the development of new theoretical understanding.

I’m not certain this is the case (who could be?) as, like the author, I’m swimming in the same sea and can’t detect the long, deep waves, just see the short ones I’m swimming though. But I hope I’m right, though I won’t live to see it.

> It’s quite possible that we are in another era of “butterfly collecting” to be followed by the development of new theoretical understanding.

The experience during my PhD indicated to me many areas are ripe for big theoretical advances using the data collected over the past 50 years. The reasons why we rarely see those advances I think are along the lines of the article. I think very few people are even trying to make sense of the huge amount of published data. Let me give a specific example from my own experience.

Here's the preprint of a paper I wrote during my PhD: https://engrxiv.org/preprint/view/740 (The cert expired yesterday, unfortunately.)

In this paper, I basically compiled a ton of data from the open literature, showed that common beliefs about a certain map in my field were in serious error (some parts of the map were absent, most boundaries were basically flat-out wrong, etc.), and developed theory and regressions for various parts of the map. It became clear to me that this sort of work, while valuable, is strongly disincentivized.

Here are some reasons:

1. To the vast majority of tenured academics, the deep literature review I did looks unproductive. My PhD advisor repeatedly called me a librarian as-if what I was doing was not my responsibility. Note the emphasis on deep in the first sentence of this point. I went a lot deeper than most people writing review articles did, tracking down a lot of obscure documents in the process. And I learned a lot in the process too! In most fields I'm sure there are many great papers that were unjustly ignored.

2. Compiling data from the open literature tends to be viewed as not science among many. Seems to be a variant of "not invented here" syndrome: "not measured here". I get that a lot of the point of a PhD is to learn how to make certain measurements, sure, and I think that motivates a lot of the unnecessary measurements made. But using existing data should be an option. And I've never seen anyone explicitly say this, but it seems to me that many people in academia believe that when data is first published, all its value has been extracted.

3. The process of actually consolidating the data into a single database is time-consuming. It may be that in terms of number of publications over the course of a PhD, doing "incremental" research has higher ROI. Data compilation makes more sense over a longer time frame in terms of ROI. Note that I'm only saying incremental work has higher ROI in terms of number of publications. Data compilation has higher ROI in terms of actual scientific value in most every case as far as I'm concerned.

4. The reception I got for this work was fairly muted. The latest fad (machine learning applied to fluid mechanics in my case) gets way more attention, even though almost every time those models are quite brittle, particularly given point number 2: The only data used is new and rarely comparable in comprehensiveness to all the published data. Using more data would of course help the ML models generalize. I remember a conversation I had with someone about machine learning in physics. I told him that I don't need machine learning because I compiled a huge amount of data from the literature and have found that linear regression works great. His response was along the lines of "That's unfair!" No, it's not. That's how science should work. But it seems to me that many people simply target the latest fad without thinking too much about the lessons of the latest fad.

It seems to me that scientists can find value in analyzing previously published data, it just takes time to find the data, compile it, and analyze it. And given that someone would probably get more publications and attention doing something else, they tend to not do the right thing by analyzing previously published data.

I wrote some similar things in this previous comment of mine: https://news.ycombinator.com/item?id=31054992

Well that’s discouraging. Thanks for commenting.

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