The Disintegration of the Academy
As a Canadian, following the recent Mark Carney prime minister win and president elect of Donald Trump, the world has become more entertaining. I’m curious to see how the world is transformed and transforms the landscape of academia. Not long after President Trump’s second inauguration in January, I attended the Cognitive Neuroscience Society annual meeting in Boston, hosting several hundreds of academics and trainees from around the world. To no surprise, neuroscience wasn’t the only hot topic of the weekend with Trump’s influence on research and education as a close runner up to the original purpose of the conference. Generally, people weren’t too happy about this political shift in the US, although the contents varied. Ironically, now that Elon Musk has helped lead the charge with DOGE, many left-leaning people have migrated to this new app called Blue Sky to boycott X (formerly twitter before it was purchased by Musk). The general trend of concern seems to be in the realm of: threats of censorship, saturation, and control of academic discourse (as expected for academics). I should add, their concerns are valid and hopefully a valuable lesson—for both sides, although a new one for the left—of how the academic entreprise can disintegrate, and the dangers of such a thing. As a student pursuing academic, this is something I’ve been thinking about for a while, so today, I will try to hash that out.
Academia is a tricky business. It’s a profession motivated by truth (or at least it is supposed to be), yet influenced by markets. As a researcher and an educator, a necessary precondition of your job is that your orientation to the research is discovering the truth. It’s a phenomena of human perception that allows that process to corrupt very easily. As a result, to even succeed in your job as a researcher, you have to look at the data without bias, and see what it tells you. Scientists, for example, spend a lot of time looking at data and conducting statistical analyses. Imagine that you’re looking at a spreadsheet full of data, perhaps thousands of data points; there’s an indefinite number of ways that you can look at that matrix because of all the combinations of the numbers that exist. Within those combinations, patterns might emerge and you can draw out a real discovery from it, but not if your orientation to the matrix is fundamentally the advancement of your career because the patterns that you’ll start to see will be perceived by consequence of your corrupted aim. That’s why p-hacking is, for obviously reasons, a cardinal sin of research.
Research careers are quite game-able in that way, and people sometimes do. You could do an infinite number of correlational analyses, and out of 100, 5 might be statistically significant. You could simply ignore the 95% that weren’t and report on the 5%. Now there’s obviously an appeal to do that, because research costs money, it takes time, and it’s what people do for a living. For example with PhDs, they need these positive results to get their degree, and say, not waste the last 5 years of their lives. Moreover, the best awards, jobs, funding are largely given to those who produce publish of high quantity and quantity. So in many ways, there’s a ton at stake with each research study you pursue. The other thing is that if you engage in such a scheme, especially for fresh grad students, you may end up chasing that fake discovery for the rest of your life, only to find out how wrong you were years later—and then your career is really over. So as an academic, you’ve got this intense wrestling match with every step to advance your career (because you still need to make a living) while avoiding corrupting yourself and steering all of your peers’ research askew.
Now I want it to take it back to the surface—scale out—and imagine where the market comes in. It’s worth thinking about how terribly a single corrupt study, as just described, can develop into something much larger and impactful. Although, a touchy subject these days, transgenderism is a decent example relevant to this idea. So you can imagine, generally speaking, in science we judge the impact of a paper to a given field by how much it has been cited. This works the same with knowledge. In the landscape of facts and even belief systems, we have a hierarchy of ideas because some ideas rely on others to be true (i.e., you need to know the colour red and distinguish it from green—among many other things—in order to know to stop at a red light). You can then map out these implicities, and realize that the more ideas that one has stacked on top of it, the more fundamental it is as an idea. That works the same way with a scientific paper. That can be lived out in the transgender idea. Today, we have transgender psychology, transgender health, transgender sociology, etc. Those all exist as major subjects that many people have built their career around, but underlying that is this question of: ‘is transgenderism real?’. One one hand, we have people that argue it is a real thing, and others that believe it is no more than a delusion. So, if the ‘data’ tells us it’s real (fake or not), the market reacts, in the same way your parents are so easily swayed by advice that follows the words ‘experts say’. If the market, and the people that influence it, believes it’s a real thing, people get jobs, projects, and funding. So you can see the effect of one idea manifesting outwards, now imagine that across the entire landscape. Ultimately, there aren’t many protections against these bad actors, besides study replication and proper investigation of ethical and genuine practice—all of which take an equal amount of time and investment with less appeal because new studies are ‘innovative’ and therefore more sought after by major scientific journals than replication studies.
We rely on the literature in many ways. At the end of the day, the disintegration of society starts with the corruption of truth. That’s the nature of truth: if anything can be true, the nothing is true, and that is just chaos. As a scientist, proper practice is necessary, for one’s career and for society. There exists an eternal pull to put oneself above the truth but as we have seen, that’s a foolish plan and doesn’t work in the long-term. I rest my case, not only for academics to acknowledge the responsibility they must bear, but for non-academics to shoulder it all the same. Every action, every thought, every word uttered is infused with this battle of corrupting truth. This is more than just a problem for scientists, this is an eternal struggle, perhaps among the most important for mankind.
