This is Jessica. Like many academics, I am concerned about what sort of new steady state U.S. universities will find themselves in after the dust settles on recent transitions. Namely, the last few years have brought funding cuts, targeted visa policy, reduced demand for grad degrees, and a general brain drain to industry (particularly noticeable in AI and computer science). It’s disorienting to think that academia has already peaked, and that the prestige ranking of the R1 faculty job over the top industry research positions (at least in computer science) might be inverting. But things feel very different than they did even a year ago. The reality of there being less money available to pay for basic aspects of research really started to hit me in the last six months. Post-covid, working on campus became less lively, but now it also feels like our collective attention is anxiously focused on Silicon Valley or Washington D.C. We hold faculty meetings where we discuss things like, Is there any way we can help local faculty members who were laid off from tenure track jobs? How will we ensure we can fund all of our own PhDs, given that TA quotas stay fixed but faculty are running out of funding runway?
To some, this is an overdue rebalancing. Nate Silver, for example, calls getting a PhD a “much worse value proposition than 20 years ago”, and predicts that elite higher ed will become “~50% less relevant in the new steady state,” which is in his eyes a good recalibration.
But it’s worth reflecting on what is lost exactly, if this dwindling of minds and resources continues. How should we think about the value of what universities provide over industry, like intellectual autonomy, or training on how to think scientifically? As a professor, I could make a list of the things that have kept me in academia–being free to work on the problems I find most important, the diversity of topics I can work on at any given time, grad students who care about doing deep work, having time to think about the best solution to a problem. But at an aggregate level, it’s less clear what the equation is.
As I was puzzling over all this, I attended a metascience conference, where there was a panel on “the social contract for science.” This is the transactional relationship dating back to at least the 1950s, by which scientists receive public funding and autonomy and society gets the benefits of scientific research. Back in the 19th century, scholars began to make a distinction between “pure” science–research unmotivated by any particular application–and applied science. The social contract takes this distinction and further presupposes a dependence relationship: what is confusingly called the “linear model”, the idea that pure science provides the well from which applied science contributions are drawn. Threaten this foundation, e.g., by letting applied science intercept too much of the resources society puts toward science, and we risk running out of useful innovations. Or so the story goes.
The social contract for science was an attempt to cement the importance of scientific understanding to society, making it an interesting counterpart to the current moment. If the actions of the current administration to direct funds away from universities, and the possibility of using AI to produce research output without understanding, are threatening our sense of what science should be, the history of science policy provides some perspective on how our expectations got shaped in the first place.
In search of the mysterious fruits of basic science
I’ve been reading the work of philosopher Heather Douglas, who has traced and critiqued the basic versus applied science distinction, the linear model as justification, and the idea of scientific freedom as limited social responsibility (see, e.g., here and here, or her book on the value-free ideal). Popularized by Vannevar Bush after WWII, in a report prepared for President Roosevelt, basic science is a reframing of pure science, presented as “scientific capital,” providing the principles and conceptions to power new products and processes years into the future. Bush called for deliberate policy to guard against the otherwise inevitable scenario where applied science drives out the pure. One of the eventual outcomes of his report was the creation of the NSF.
But despite the pragmatic nature of basic science espoused by Bush, as a derivation of pure science, it is hard to separate from less tangible values. One is that scientific understanding is a good outside of practical application, at both the individual and societal level. The earliest advocates of pure science associated it with being closer to God. Post-Enlightenment, this view gave way to a more secular superiority complex, which implied the strong character of the pure scientist, who chose to eschew wealth. “The highest occupation of mankind”, Henry Rowland called it in his Gilded Age era essay, “A Plea for Pure Science,” which bemoaned the vulgarity of attributing scientific greatness to the applied scientist rather than the pure.
From a less moralistic point of view, we’ve been encouraged to believe that a society that has rigorous ways of understanding the world is better off over one that doesn’t. Throughout history, understanding the laws of Nature has been portrayed as a good in itself, along with an intellectual life. From this view, by educating people on how to pursue deep understanding of the world, universities provide the general good of scientific thinking to society. If we believe in the intrinsic value of reading, writing, or intellectual discussion, then it would seem we should value the university as a place that provides the kind of timespan and environment needed to develop these skills.
Some argue that the university has come to serve too many conflicting purposes (research engine, job training center, credentialer, incubator of coming-of-age experiences), and should go back to its classical roots: training in oral reasoning and rhetoric, ethics and moral judgment, historical analysis, and the cultivation of taste and discrimination. This may be a useful refocusing, but it offers little consolation for the fact that the elite research infrastructure that helped this country establish and maintain scientific leadership for decades is in the process of being gutted.
If we take our intuitions from the linear model, we might protest that innovation will suffer if universities’ research purposes are deprioritized. The post WWII science-industrial complex expanded the presence of basic research in industry, but studies suggest that the knowledge generating role of corporate R&D has been on the decline for years. To the extent that basic research is the supplier of downstream applications, it would seem we need universities more than ever.
But the distinction between basic and applied science that’s become synonymous with how we envision science has never been airtight. Critics questioned how an institution could be built around a distinction that seemed to amount to little more than a difference in intention, since applied research sometimes produced important new general knowledge, and pure science contributions sometimes had direct applicability.
AI research is a recent example. Not only is serious money being made without necessarily requiring advanced degrees, research positions do not require PhDs. By some accounts, passing 30 years old puts one in the older demographic of researchers at frontier AI companies. Yet much of the visible innovation in frontier model development has been heavily concentrated in industry labs, including transformer models, scaling laws, and AlphaFold.
Of course, AI owes much to academia. The amazing thing about deep learning and LLMs, to anyone who was paying attention to NLP before these developments, is that after many years of AI research contributing interesting questions but lackluster results, the technology finally seemed to work. Would we have had the foundations for deep learning if perceptrons had not been stubbornly pursued by academics like Frank Rosenblatt at Cornell early on, picked up again in the 1980s by Rumelhart and McClelland’s Parallel Distributed Processing group, despite multiple periods during which consensus said connectionist approaches were unlikely to pay off?
The challenge is that arguing that “someday the research will pay off,” without being able to point to any hard evidence that basic research is, on average, worth the investment, is not such a convincing argument. According to Douglas, studies have been attempted to show the payoffs of basic research, but without very impressive results. Uncertainty about what time scale we should expect between discovery and application makes this kind of exercise difficult.
At the same time, it’s hard to dispute that monetary incentives can sometimes discourage exploration that would eventually pay off. In evaluating the role of academic research to AI progress, we should keep in mind the uniquely massive private investments AI companies have received, and be cautious using it as a general example. It would be premature to conclude that because progress (in terms of models’ standalone capabilities as measured by benchmarks) doesn’t seem to depend much on academic research at the moment, cutting off academic research would be immaterial. Particularly unfortunate about the historical contingency of frontier companies defining the direction of the field is that they are focused on a pretty narrow space of methods, evaluations, and design ideas. But the power and resources they hold give newcomers to AI research the impression that ideas outside this narrow space aren’t important.
Indulgence, autonomy, and social responsibility
As suggested above, it’s always been tempting to bring moral judgment to bear on the basic versus applied research divide. The latest moment with AI research is no exception. Does the moral high ground belong to those who are staying in academia, underfunded or not, to preserve university culture and their autonomy from corporate interests, or those who are willing to give up a comfortable job to shape the impact of AI as a product in the world?
From one perspective, the academy, as a haven for basic science, has always been at risk of being seen as indulgent. In practice, building a scientific career is in many ways a process of identity development and fulfillment for the scientist. Historical pure science rhetoric associated the pursuit of scientific truth with self-realization. But talking about personal fulfillment does not go over well when your opponent is promoting the idea that science could do more direct good for the country or humanity. Academic scientists have always been at risk of coming off as being self-indulgent, insular, or dilettante when they defend understanding for understanding’s sake. The current political moment is just rehashing old themes.
Another unfortunate historical association of basic science is with insularity and shirking responsibility. After WWI era advances in chemical warfare and explosives, the social responsibility of the scientist became a much greater concern. Philosopher John Dewey came down sharply on the idea that an autonomous space for pure science, unhindered by societal concerns, was something to strive for. Instead, he argued that this impetus to protect pure science was partly a convenient abdication of moral responsibility for the downstream outcomes of research, a “shirking of responsibility.’’
Dewey’s concerns came at a time where philosophy was itself seeking to be more scientific. According to Douglas, Dewey’s views on how philosophers should approach science–through greater integration of societal concerns–lost out to the argument espoused by Bertrand Russell, who instead valorized the “disinterested intellectual curiosity which characterizes the genuine man of science.” The latter view became the more accepted one, and our definition of scientific freedom arose in tandem with expectations of limited social responsibility. It’s not particularly surprising, then, to encounter beliefs that academia is not the place to go if you want to have impact in the world.
At the same time, it seems hard to deny that at this point of time in AI, where we have a large imbalance of power and resources, there is something to be said for the autonomy afforded by the university or nonprofit. Some beliefs about AGI coming out of Silicon Valley border on religious. My biggest concern if I were to join an AI company at this point in time would be losing my ability to think for myself about what problems deserve priority. Having greater agency and impact are attractive, but not if they come at the expense of one’s internal compass or values. As Brendan McCord said recently, “Autonomy is different from agency. Agency is getting things done…You can be more effective than you’ve ever been, and you can be less the author of your own life than you’ve ever been.” Against the groupthink of Silicon Valley, the value of the intellectual autonomy academia provides does feel real. Though it’s unclear how valuable this autonomy will continue to be if academics and others outside the big labs can’t retain enough funding or visibility into frontier model development to remain relevant.
The problem with defining progress as prediction and control
In a 2014 article called Pure science and the problem of progress, Douglas suggests that if the pure/applied science distinction doesn’t survive scrutiny (which she argues it does not), we’re left with an account of scientific progress based on our ability to predict, intervene, and control our world. But this is not a definition of progress we should be content with:
“Any increase in the capacity to predict or control the thoughts and feelings of human beings would count as scientific progress. An increased capacity to destroy human subpopulations (through, say, targeted pathogens) would count as scientific progress. Developing new heinous capacities would count as scientific progress. Unlike Rowland, we should have no illusions that greater causal efficacy, greater power of intervention, will in fact always provide a better society.” (p. 63)
If misaligned AI, our own creation, changes how we view ourselves and the world, if it convinces some of us it has all of our best interests at heart even as it feeds our insecurities, or pursues its own goals in the background, is that scientific progress?
Douglas argues that judging real progress requires society to weigh in. When it comes to AI, this is happening through pushback against data centers, and the pace of AI progress, and the culture of Silicon Valley. Adoption matters too, but can’t be a substitute for evaluation. We need institutions independent of the companies to help interpret what’s going on. In the midst of changes to so many of our current institutions, we should expect the story we ultimately tell to take time to sort out.
In the meantime, defending academia as a category of research, or a moral standard, is a dead end. What seems more reasonable to advocate is a set of conditions — time, autonomy, training in scientific judgment, the evaluation of new approaches independent of their profitability. The value of these ingredients isn’t easily summarizable in some neat story, because what drives scientific progress is not that simple. But institutions that help society judge what’s been achieved seem worth defending.
As long as your post is, it is inadequate for all the issues it raises, and it raises too many issues. Basic vs applied science, value of college degrees, employment conditions for faculty, student expectations, politicization of academia, AI, universities and the private sector…. (I may well have missed some). Clearly these are not separate issues as there are complex overlaps. I can’t begin to provide organized thoughts – except one. At my ripe old age (still teaching, however), I’m glad that I’m not starting my career in academia now. I feel bad for those who are.
You ask: “what sort of new steady state U.S. universities will find themselves in after the dust settles on recent transitions?”
I respond: “what makes you think there will be a steady state?”
Hi Dale,
My post is intentionally big picture. I don’t claim to have answers on how we should be defending academia. But it’s clear we’re in a period of transition and I think it’s worth reflecting on the stories that have been built into our funding models up to this point.
Steady state is relative. For most of my academic career, perceptions of what universities are for have seemed fairly stable. Now we’re seeing a lot more questioning and shifts in momentum. I expect things to be in flux for awhile. What the future of the university should be/will be is an open question.
I think the essay holds together quite well.
I do think there’s a bit of confusion of two overarching questions: “what’s the point of research universities?” and “what’s the point of publicly supported non-applied research?” One could have the latter (e.g. National Labs, imagined differently) without the former. I’m not saying that’s a good idea, but one can imagine advocating for one and not the other. In fact, I think to a considerable extent the current US administration’s attacks on university research are by-products of attacks on universities.
Good point on the distinction. I agree, US admin attacks seem wrapped up with perceptions of what happens at universities, not all sites of basic research.
The Trump administration is trying to close the National Center for Atmospheric Research (a judge has stopped it for now, but everyone expects significant budget cuts and some staff have already left). The administration has proposed halving the National Laboratory of the Rockies, which was formerly known as the National Renewable Energy Laboratory but changed its name because the administration says they’re not supporting renewable energy anymore. Lawrence Berkeley National Laboratory is slated for significant cuts in programs related to energy efficiency research, but may make up part of it through additional genome-related research.
Basically, although in principle the National Labs could be treated better than universities, in practice they are being treated roughly the same, at least when it comes to research funding.
Yes, agreed. One might factor in philanthropic/charitable funding of biomed in the US which isn’t so far below government levels. Likewise in the UK. One assumes this will remain intact although continued charitable support of Uni research will require that appropriate Uni infrastructure remains intact and that gifted researchers choose to remain in their Uni’s. Charitable funding outside of Uni’s (e.g. Broad Institute) is potentially exempt from concerns around diminishing of Uni status and resources.
However, it isn’t really. All those young researchers working in research institutes and industry need to learn their trades and it’s difficult to see how this can be maintained outside of Universities. Gifted researcher who might find positions in National Labs, research institutes like the LMB in Cambridge don’t spring from the ground ready made – they are identified (or self-identified as developing a passion for research) largely within the Uni system as large numbers of undegraduates progress to smaller numbers of PhD students to smaller numbers of post-docs. I’m reading Venki Ramakrishnan’s book (7 out of 10 IMO) on the determination of the structure of the ribosome in which he describes his research journey. Although he was extemely research-focused (i.e. positions largely in research centres as opposed to the research/teaching/admin/supervision roles of most Uni scientists), he wouldn’t have got anywhere without an initial progression through University(ies).
Phil: so in other words left-wing research priorities are being cut. I’m having trouble finding my tear glands.
That’s what I see throughout this thread: the confusion between “research” (general) and “research focused on left-wing interests and beliefs”. But that’s not surprising because the leftish scientific establishment like the entire left has come to view its priorities as *the* priorities.
Meanwhile several companies are gearing up REE extraction, processing and magnet production – a very real and serious priority compared to the piddling energy efficiency gains for refrigerators and efforts to eliminate gas stoves pushed by the previous admin.
hello hello hello in the echo chamber! :)
Anon:
You describe the National Center for Atmospheric Research as a “left-wing research priority.” On the face of it, this sounds like an odd characterization. But I think it’s a telling example of the increase in partisan polarization that such a claim would be made at all.
Quote from the blog post: “But it’s worth reflecting on what is lost exactly, if this dwindling of minds and resources continues? How should we think about the value of what universities provide over industry, like intellectual autonomy, or training on how to think scientifically?”
Training on how to think scientifically? Does, or should this, involve things like reasoning and logic? If so, perhaps we have already stumbled upon something worth thinking about, and even something that might explain why some think academia has messed things up itself or why some think academia is a waste of money and time and energy.
Doing some research on logic and reasoning, or pondering about the possible crucial role of such things in an education at university has the additional benefit that it perhaps does not need any grant money or hiring of PhD’s or such things. This is something that could be done by staff of universities themselves, at least to a certain extent or in a certain way. Perhaps something to bring up at a next meeting…
For instance, a simple online questionnaire can be developed to try and get a better picture of whether or not students have mandatory logic and reasoning courses in their curricula. Or, staff members can simply ask whether students at their own universities have to take mandatory classes in logic and reasoning (or whatever word is more appropriate to use here). Or, staff members can take a look at the total curriculum of an education one is involved with to check whether or not this curriculum includes something like a mandatory course in logic or reasoning (or whatever word is more appropriate to use here). Or, perhaps a manuscript can be written about the importance of this all, for countless things related to research, which also seems to me to not need any grant money or hiring of PhD’s.
Here’s a section of one of my manuscripts for some more possible pondering (written without being employed at a university and without having received any grant money):
“The more I think about it, the more I reason that less might be more, and that simple might be better (cf. Cohen, 1990, p. 304-1306). I think a course based on fundamental topics and concepts like logic, reasoning, and argumentation (also see Sayers, 1947) would have been more valuable than most of the courses that were part of the curriculum of my education. Courses which involved papers, information, and “knowledge” (nine-tenths of which I will never use again) of questionable quality and validity given all the problematic issues mentioned in the introduction. I think logic, reasoning, and argumentation are extremely important with regard to comprehending scientific writing, integrating scientific findings, critical thinking, hypothesizing, designing experiments, (re-)formulating theories, writing papers, etc., which in my view further justifies an
entire specific course. In light of this all, I have wondered more and more why I wasn’t taught (much) about these things.
After some searching I discovered an online version of a book by Dowden (2022) titled “Logical Reasoning”. The table of contents includes: “what is a statement?”, “what is an argument?”, “rewriting arguments in standard form”, “deductively valid and inductively strong”, “improving your writing style”, “distinguishing deduction from induction”, “what is logic?”, “logical forms of statements and arguments”, “reviewing the principles of scientific reasoning”, etc. I think learning about these fundamental things, in a certain order, might be of tremendous value. That a course on these topics might be useful is underscored by Mulnix (2010) who notes that far too many college undergraduates have yet to achieve proficiency in thinking critically, and that many students are unable to recognize even the simplest of evidential relationships between statements (p. 11). Mulnix
(2010) further notes that “This appears to suggest that the fundamental concern of any critical thinking course ought to be teaching students to ‘grasp’ inferential or evidential connections.” (p. 11). A course that covers the topics and concepts discussed in the book by Dowden (2022), that involves learning names for argument patterns and fallacies (cf. Mulnix, 2010, p. 12), that involves rewriting arguments in “standard form” (cf. Dowden, 2022, p. 41) or using “argument maps” (cf. Mulnix, 2010,
p. 12; van Gelder, 2005, p. 44-45), and that involves practicing by editing papers (Mulnix, 2010, p. 13) might be very appropriate and useful. I wish such a course had been part of the curriculum of my education. I wonder why it wasn’t.”
References:
Cohen, J. (1990). Things I have learned (so far). American Psychologist, 45, 1304-1312.
Dowden, B. H. (2022). Logical Reasoning.
Mulnix, J. W. (2010). Thinking critically about critical thinking. Educational Philosophy and Theory, 1-16.
Sayers, D. L. (1947). The lost tools of learning. Paper read at a Vacation Course in Education, Oxford 1947.
Van Gelder, T. (2005). Teaching critical thinking: some lessons from cognitive science. College Teaching, 53, 41-4.
About that course in logic, reasoning, and argumentation (or whatever words are more appropriate to use). This is from the blog post from a while back titled “Well, today we find our heroes flying along smoothly…” about the retracted Protzko et al. paper:
“That would mean spending more time beyond that I’d already spent going through the OSF to write one of my blog posts to sort through the paper’s arguments and consider whether they could possibly hold up. None of this is at all connected my main gig in computer science.”
All that time spent might have led to noticing possible flaws in this particular paper’s argumentation and reasoning, and could possibly be used as a concrete example in such a course. Maybe argument maps or rewriting arguments in standard form might make clear why the authors could never properly conclude, or suggest, what they concluded based on the design and characteristics of the study.
Maybe there are some people (e.g. one or both of the authors of the “Claims About Scientific Rigour Require Rigour” paper) who might have useful and relevant things to add to such a course, or provide further examples. Further examples could even be found in other recent so-called meta-scientific papers (I can possibly point to some). Or examples can be taken from the more general reasoning and argumentation concerning the so-called replication crisis, or the reasoning behind promoting open practices, or the explanation of why researchers might perform research sub-optimally due to so-called publish or perish processes. Perhaps these all contain flawed or incomplete or sub-optimal reasoning and argumentation, which can possibly be made more explicit and be used as relevant scientific examples in such a course at university.
Quote from the blog post: “The reality of there being less money available to pay for basic aspects of research really started to hit me in the last six months.”
About grant money, and hiring PhDs, and that retracted Protzko et al. paper: Perhaps it might also be useful to mention that certain things can be achieved without much, or any, grant money. For instance, the general idea of researchers replicating each other’s work as has been done in Protzko et al. if I am not mistaken is an idea that can be thought of without hiring of PhDs or without any recevied grant money.
I think I have done so, or at least somethings similar, back in 2013 for instance:
https://groups.google.com/g/openscienceframework/c/2nhHMdGGhrw
And I wrote something about that general idea on this blog as well again back in 2017:
https://statmodeling.stat.columbia.edu/2017/12/17/stranger-than-fiction/#comment-628652
Perhaps this makes some things clear, for instance that it might be possible to come up with ideas without any grant money and that one can share these ideas without publishing in a so-called top journal.
It may also make clear that it might be important and useful to just think about things.
Given what has happened concerning that Protzko et al. paper, and thinking about the the possible involved grant money and all those years of work, it might also be interesting to ponder what would have been possible, and would might have happened, if the authors would have just written down the general idea, left aside the need to provide evidence for the possible usefulness of certain things, and let others possibly try the idea and see what would, or could, come of that…
Thanks Jessica —
That’s a really interesting read.
You might be interested in this podcast:
https://open.spotify.com/episode/3r7hvlkQLbM0in99Bzn3YP?si=xVa3O3hzQQWU3Sqn0rYk0A&utm_source=copy-link
In it, Wright and Sebastian Mallaby talk about AI’s shift from deductive, rule-based systems to inductive, data-driven ones (deep learning). It got me thinking about your pure/applied dichotomy — not because I think there’s anything inherent about “deductive = pure” and “inductive = applied”, but because there seems to be an interesting parallel or overlap between the two.
I’m thinking of deductive systems as being more bounded by whatever rules and representations you’ve explicitly built in, which feels a bit like the academic/pure side: constrained, principled, and working within a discipline. Whereas inductive systems, by contrast, it seems to me, can scale with however much data, compute and experimentation you can bring to bear, with perhaps less of a natural ceiling. And there, a competitive advantage can come from who has the resources to scale furthest. That feels more like the applied/industry side, driven by open competition and external objectives rather than internal disciplinary constraints.
I’m not suggesting this as a rule-bound dichotomy, but just a pattern I noticed while reading your post. I’m thinking the applied side might favour more inductive approaches, while also operating in more competitive, less bounded environments, and those two things may reinforce each other.
It seems to me that a period of dust-settling is required before a clear view of these issues can be obtained. Certainly in the US, extreme government antipathy towards the University sector combined with something of an onslaught of misinformation and contrived conflict is overlapping with what seems to be a pretty sudden intrusion of AI into the mix. But the government may well change and we really don’t know how AI will play out, although we might have a pretty good idea where it’s going in the next couple of years. It just doesn’t seem like a good time to be coming to fundamental or direction-changing conclusions.
The “pure”/”applied” science dichotomy, is easier to parse from the perspective of biological and physical sciences where the value of pure science is still placed highly I think. Apart from anything else the pharma/health sector relies heavily on innovations made in a pure science context and so has an interest in supporting this. It seems to me that this element of academic science is likely to remain vibrant in Universities. In any case for all the potential applications and intrusions of AI into existing human expertises, academic or otherwise, the only way to determine whether this molecule inhibits that protein, or if this particular metallic mixture is a superconductor at such and such a temperature, is to do the experiment. That’s not going to go away in the forseeable future.
Although academic scientists continue to be comfortable studying phenomena in a “pure” sense to understand how elements of the natural world work, increasingly funders of “pure” science require some (at least lip-service) indications of potential transferable applications in grant applications. That does seem to be the way things are going, although this may simply indicate a loss of nerve on the part of science administrators that funding pure science will lead transferable applications.
So it’s possible that there might be a retrenchment in the Uni sector with a consolidation around Health/Physical sciences. That’s already happening in the UK. Still University academics, scientists and all, need to continue banging the drum for the less apparently “profitable” values from a University education that include cognitive honesty and stamina, appreciation of nuanced interpretations, ability to perform deep research and to create coherent and honest summaries, cooperative learning and working, taking responsibility for one’s intellectual development and so on. It’s not obvious that there’s a whole lot of support for this outside of the Uni sector.
Academic funding has been slowly crushed since the 1980s. Reagan took a lot of funding from universities and forced them to depend on private money and raise tuition.
Also, I agree that AI has only revealed the flaws in the system that already existed.
See also: an obituary for academia, written in 2024. https://journals.sagepub.com/doi/10.1177/10596011261431423
Wow, that article!! Though all the versions I see online say 2026, not 2024?
Sorry, typo. The article (written in 2026) ways that academia died in 2024.
“Says that academia died in 2024”. I can’t stop making mistakes.
The PI had a slew of retractions for editorial misconduct and data fabrication just months after this article appeared (https://casrai.org/news/sage-retracts-griep-papers-peer-review-conflict). Given their specific plea to stop treating retractions as a ‘scarlet letter,’ it makes you wonder if this whole editorial was written as some sort of attempt to mitigate the poor PR.
That said, the idea that the academy is dead – at least in the way we like to think about it – seems pretty spot on.
This must be the week for strange unexplained research phenomena. The article (the obituary for universities) is interesting enough, but the retraction/firing of the lead author for editorial conflict of interest sort of fits right in with causes of the death of the university. It is almost like Ariely writing about dishonesty. Or like everything Trump says his enemies do (e.g. weaponizing the Justice dept). Are we now living in a world where everything has these double-meanings? [also the Polson manuscripts and books and AI].
As already pointed out by others, it might be relevant to point out that the author of this article has recently been found to have committed quite substantial violations to scientific integrity (including data fabrication and corruption of the review/editorial process) and has been dismissed from his position at my university for misconduct (not actually because of the research misconduct but because of fraudulent expense claims). Of course, important to keep separating the message from the messenger, but it might be interesting to read it with that in mind.
Really a sad case, to be honest. It prompted me to go off on long rant that was published by our university newspaper, maybe interesting for some of you. But be warned, it’s long, rambling wall of text. I did throw in an Andrew Gelman quote, though, so there’s that at least.
https://www.voxweb.nl/en/academic-fraud-may-be-the-symptom-of-a-much-more-systemic-problem
Jessica:
I pretty much agree with everything above, and I have two further thoughts:
1. Your discussion of the social contract of science reminds me of something I wrote a few weeks ago regarding David Agus, the USC medical school professor who published a book full of plagiarized material and also burned through a few millions of some rich guy’s money on a failed business scheme:
And then:
2. I also thought about the story that just came up yesterday, about the apparently delusional University of Chicago business school professor who (a) seems to think he’s proved the Riemann hypothesis, and (b) flooded the SSRN preprint server with over 200 articles over a period of less than a year. And they weren’t short articles either: it’s clear that much of the material was produced by chatbots and is indigestible by mere humans.
On one level, this is a simple case of a professor exploiting the system, taking a bunch of steps which individually might seem reasonable (using a chatbot to explore an idea, using a chatbot to summarize the literature, posting papers on a preprint server) but doing them to a ridiculous extent beyond reason.
The interesting thing about this case, though—what really makes it stand out to me—is that the professor in question has no instrumental reason for doing this. Usually when we hear about computer scientists flooding conferences with unwanted papers, there’s a reason: the investigators are padding their CV’s as part of a job-seeking arms race, or they’re trying to help out their graduate students who are in their own publication arms races, or they’re trying to help out their undergraduate students who are in the publication arms race to get into grad school. It’s the “bad incentives” that we’re always hearing about. But this Chicago professor has been tenured forever, and many of the papers are single-authored. He’s just doing it to do it, because that’s what professors do, as he sees it. For him, AI is a productivity tool that’s gone out of control and taken over his life. Kind of a metaphor for our entire society, no?
“Further, it will not be amiss to distinguish the three kinds and, as it were, grades of ambition in mankind. The first is of those who desire to extend their own power in their native country, a vulgar and degenerate kind. The second is of those who labor to extend the power and dominion of their country among men. This certainly has more dignity, though not less covetousness. But if a man endeavor to establish and extend the power and dominion of the human race itself over the universe, his ambition (if ambition it can be called) is without doubt both a more wholesome and a more noble thing than the other two.”
— Francis Bacon, Novum Organum, Book I, Aphorism 129
Academia only works well for a small number of people. Most students, teachers, and taxpayers are screwed to varying degrees by the current setup.
If I were in academia I’d adopt the mantra “evolution beats revolution” and be working hard to evolve into something better before it’s too late.
It seems to me that not addressing the other reasons universities and in particular science funding is under attack is potentially over-stating the direct attack on pure science funding. To what extent is it collateral damage in the culture wars? Retribution against perceived tribal antagonists? How much of that is deserved, given the lopsided ideological distribution within universities, which sometimes manifests in some starkly antagonistic and starkly negative value-added ways itself? What are the chances the decline in basic science funding reverses if a new political party comes into power? Certainly not 100%, and certainly not 0%, but I don’t have a good sense for where in between.
Will,
I don’t know, but until very recently, spending on higher education and research, and autonomy of higher education and research, were bipartisan policies, at least on the national level. I attribute this to a mix of three factors:
– Geographic coverage: universities in all 50 states, including respected institutions such as University of Alabama at Birmingham in solid Republican states.
– Broad appeal: so many people having college students in their families.
– Economics and public health: a general desire not to kill the golden goose.
But in recent years education and science have become politically polarized, as with so many issues.
I’d love to read Lizzie’s take on this as an academic ecologist. I don’t think ecology as a field fits the pure/basic research then applied research/development the way AI, pharmaceuticals, and other biomedical fields do.
I’ll post my rather odd (agency but former academic) perspective later tonight, as I’m off to the beach to be a real life outlier: still playing volleyball at my advanced age despite the negative effect on my longevity.
Tom:
Not to worry about it; just clean the palate with some pole vaulting after the volleyball game is over.
That was a long post, with lots of long-winded replies.
It’s not that hard: the US government, as led by the Republican party, is hell-bent on destroying US higher education and science.
If you aren’t out there stumping for Democratic candidates and advocating for progressive policies, you are part of the problem.
It’s that simple.
A scientific establishment which, as this comment shows, has aligned itself with one political party, should not be surprised when the other party does not have a high opinion of it. It would be easier to survive this if “the science” was seen as equivalent to objective truth, but a series of events dating back at least as far as the replication crisis has ensured that this is not the case.
A. G. –
> “A scientific establishment which, as this comment shows, has aligned itself with one political party…”
Consider that the direction might run the other way – a party aligning against science, rather than science aligning with a party. It used to be that “the left” was, if anything, the more distrustful side (or there wasn’t a clear signal). More recently the distrust has become concentrated on the right, and the timing might well have tracked the growth of the religious right and the fights over evolution, abortion, and stem cell research more than it tracks any change in how scientists do their work. Might not be a coincidence.
It was probably the right’s hatred for the results of the climate scientists that drove them to their anti-science insanity. And then COVID pushed them fully over the edge. Of course, Drumpf’s insistence on his own “truth” is now the core of said insanity.
But, whatever, the Republican really are in the midst of dismantling the US government, starting with science and foreign aid. And we’re feeling the brunt of it.
Your job depends on voting the Republicans out.
(The Republican hatred for minorities, immigrants, and anything that helps the poor, the weak, the sick is the _moral_ argument for voting them out. But that’s off-topic here…)
David –
The trend started when climate science wasn’t very controversial and showed little or no political signal. But no doubt, climate science is the poster child for the phenomenon.
It’s interesting that with climate science, the father put you go on the ideological right the more confidence people are that there’s no risk from anthropogenic CO2, even as they display more ignorance about the science. (I could dig out the links if anyone’s interested).
The entire trick that makes science work is *not* trusting anyone (including yourself). This is hard/rare because it goes against the human instinct to follow authority/consensus.
Without that lack of trust, its just another religion.
Indeed, way too many people involved with “science” today hate actual science (doing replications, working out the precise consequences of various theories to compare them, etc).
Anoneuoid –
> “The entire trick that makes science work is *not* trusting anyone (including yourself).”
The problem is that not all people are as smart and well-informed on all subjects like you.
That means that they have to have some level of “trust” in the expertise of smart people who spend decades studying and researching highly technical subjects in depth.
It doesn’t mean having blind trust, but that at some level when you, yourself, can’t trust your own ability to understand topics you have to assess probabilities of those experts being correct and provisionally assume they’re more likely right than wrong.
Sometimes it doesn’t work out. But the failure mode isn’t just “trusting too much. Plenty of people go the other way, treating any instance of science getting it wrong as reason to distrust the entire enterprise. Both are calibration errors.
No need to know the details if someone predicts the exact progression of an eclipse, makes a rat glow under blacklight, pulls a ship out of the water by themselves, makes a car run, etc.
That is the point. People don’t need to understand or even read all the details when it works.
Anoneuoid –
> “That is the point. People don’t need to understand or even read all the details when it works.”
No. Again, you miss my point. I can’t *see* a probabilistic, multi-decade projection of a marginal increase in global temperatures “work.” I don’t live long enough to watch that play out as signal above noise in the way I can watch an eclipse or a car start. Cherry picking examples doesn’t address the issue.
Joshua:
You can’t see global warming with your own eyes, but you can see the predicted curves compared with the measured curves with your own eyes. You can also see the graphs showing that e.g. the upper layers of the atmosphere are cooling, which was predicted by climate models before it was observed, with your own eyes. I think this is the essence of Anoneuoid’s point.
https://theconversation.com/5-forecasts-early-climate-models-got-right-the-evidence-is-all-around-you-263248
Joshua;
I think I just realized what you were getting at. For data that span decades, you must go some extent “trust” the compilers. But I think this is a lower level of trust than you are saying in your comment. You can always check with new and independent methods, and all you should need to check the quality of predictions and postdictions is a well-constructed graph.
Meese, Anonymous –
I should have been clearer. My point was about signal vs. noise in the projected *rate* of warming, not direction. While the stratospheric cooling example is a directional prediction and can be confirmed by observation, it’s a lower bar than confirming rate.
Rate is harder on the trust question, since the projections aren’t a single trajectory, but a distribution with confidence intervals where most of the probability mass sits in the middle. Even with a long enough observation window I’m asking not just “does the observed rate match a line,” but “where does the observed rate fall within a probabilistic band.”
Even people who broadly accept that band, like self-described “lukewarmers,” often dispute where the most probable value falls within it, arguing for something closer to the low end than mainstream estimates. Adjudicating that isn’t something I can resolve by looking at a graph. It turns on the technical methodology. I can’t independently verify how climate sensitivity is estimated or how the underlying data are handled. At some level, I have to provisionally “trust” or “distrust” the people doing that work, with understanding that my own judgment is limited.
Joshua:
I feel like if you have reached the point in the argument where you start arguing about fine differences in the rate, you’re not in a good spot. You can stick to the essential aspects, and this works for explaining the subject to disinterested laymen and arguing with deniers. What I mean by this is as follows:
Most laymen want a simple explanation of the physics. This can be given, and it leads to predictions in the ballpark of what is observed (the agreement is actually quite good). This type of simple argument is usually all that is required. Such arguments can be useful for scientists too, they reveal the essential details and assumptions and increase our confidence that we truly understand something.
Here is an example of such an argument, which you can probably explain to laymen by doing a simulation: https://www.nature.com/articles/s41467-024-48469-w
You can come to good conclusions and get rid of a lot of nonsense even with estimates that are only good to within a factor of 5-10, perhaps surprisingly.
This also works for the human consequences of climate change and the “what we need to do about it” part. See e.g. “Without Hot Air”, a book abut decarbonization that uses this approach. https://www.withouthotair.com/
[I think all of the economic projections are pseudoscience, and think that order-of-magnitude estimates like this are the *only* ones that can be done for the human consequences, but I digress.]
“The numbers are uncertain” stops being an argument in the face of this approach. Even granting uncertainty, the models work!
Lukewarmists try and cast doubt on all the models based on small details. So don’t let them do that.
Simply say to them “fine details don’t matter so much”. If they still want to deny the essential points, that’s on them.
Joshua:
To be clear, my first line should be read in the context of an argument with a denier.
Anonymous –
I think you’re addressing a different question than the one I raised. My point wasn’t about how to argue with lukewarmers – it’s that I rely to some extent on trust, for the reasons I explained, when it comes to assessing rate.
As for other people, I’m not talking about convincing them either. It’s about what actually happens. For most people, views on climate change are primarily shaped by who they trust, and that makes sense – there’s no clear signal for them to see, and it’s reasonable to expect they don’t see one. The science is forecasting a rate of change that isn’t “seen to work” on the timescale most people live their lives. That’s a built-in problem for convincing people to support mitigation or adaptation policy. They aren’t going to be convinced by something they can see work, because the decadal rate of change won’t show that – unless, as could happen, and as the science allows for, the change comes in the form of a punctuated equilibrium rather than a smooth trend.
Joshua:
I think the points I raised are relevant to this. When you use a simple model that makes predictions that nevertheless agree well with reality, you don’t need to trust anymore. You can verify it. I would go further and argue that this is actually a core part of science.
“For most people, views on climate change are primarily shaped by who they trust, and that makes sense – there’s no clear signal for them to see, and it’s reasonable to expect they don’t see one.”
That’s fine and reasonable, but putting simple models out there can convince more people, and then the people who trust those people will also be convinced. Telling people “trust the experts” doesn’t work, especially in our era of deep (and justified) mistrust in the establishment.
Maybe the model should be prefaced with the headline “ROGUE CLIMATE SCIENTIST DESTROYS ESTABLISHMENT IDIOTS AND THEIR HUGE COMPUTER MODELS”. ;)
Joshua:
I suppose, ignoring the issue of models entirely, one way of convincing people to support mitigation policy is to show that it saves them money. Showing people that cheap solar panels can save them money on their power bill is one example.
‘The science is forecasting a rate of change that isn’t “seen to work” on the timescale most people live their lives.’
With the rise in more powerful hurricanes and wildfires, that statement isn’t as true as it used to be.
JJ –
> “With the rise in more powerful hurricanes and wildfires, that statement isn’t as true as it used to be.”
This connects to my larger point. Extreme events are inherently rate/frequency phenomena, and the signal/noise dynamic I’m describing is, if anything, more relevant for them than for average warming rate. Individual severe events are rare and highly variable, so distinguishing a real trend from natural variability takes an even longer observation window.
IPCC work bears this out. It has low confidence in detecting long-term trends in overall tropical cyclone frequency or intensity because severe storms are rare and historical records aren’t long enough to separate trend from noise. Wildfire trends are better supported regionally, but even there the IPCC’s confidence is about fire weather conditions specifically, not fire outcomes – which are also driven by land management and ignition, not climate alone.
That’s why most people’s views on this end up shaped by who they trust rather than by anything they’ve “seen work.”
Hurricane or wildfires might feel like more than before, but it may not be a true signal. Then if they don’t happen for a while it feels like there is no signal. Those kinds of noisy, ambiguous experiences leave room for trust more than actual observation go be more influential.
While some of this could be solved by dissolving the basic/applied science divide I’m not really clear what that looks like, in individual research initiatives and as an aggregate corporate or academic system that works based on this structure of research.
IMO the basic/applied science divide is already somewhat dissolved, since everyone doing basic science does so with the hope (however fleeting) that something of value will come of it. In reality, much of the research done in Uni labs, especially small ones, is really pure research (tho always with the hope of finding an application – which might actually be written into the grant application if the research is directly funded).
Nowadays much applied research is done either in industrial settings or within consortia, but it is usually easy to see the pathway by which pure research led to the applied research. A good example is in the treatment of “baby KJ” last year with a personalised CRISPR-based gene editing to attempt to correct a metabolic enzyme deficiency. The bacterial Crispr system was discovered by seperate Japanese and Spanish researchers who noticed odd repeat sequences in bacterial genomes (totally Pure research). This discovery was built upon by researchers who showed that the Crispr could be developed into a system for accurately cutting DNA sequences (Pure research with obvious Applied research applications). Ultimately, the gene editing developed into a technology developed within a large consortium involving University of Pennsylvania, Childrens Hospital of Penn., NIH researchers, the Innovative Genomics Institute and Industrial contributers (clearly Applied research!).
That’s quite typical and may likely be increasingly so. The development of a technology for RNA modification to limit immune reaction (Pure research with obvious Applied research applications) that lead to anti-Covid RNA vaccines (clearly Applied research) is another example. That’s why I said in my post above that the “pure”/”applied” science dichotomy, is easier to parse from the perspective of biological and physical sciences.
I came late to this. Maybe I missed it, but no one seems to have brought up the issue of externalities. Some research programs have more potential external benefits than others, which leads to a straightforward economic case for subsidy, at least. I would also emphasize the importance of flexibility and resilience of research programs: there’s a social interest in this that isn’t captured by direct users of the output of specific programs.
Finally, about Dewey. I think he’s ultimately right, but the problem is getting there in a defensible way. For me, it comes down to incorporating specific social purposes alongside profit as objectives of the firm. I’ve been writing about this in other contexts; e.g. see my recent AI piece in Jacobin. Dewey was one of my guides as I was trying to figure all this out. In a world with only profit-motivated firms, of course, only public or nonprofit institutions offer an alternative venue for science, so we have to defend them.