A 2022 survey by Nature found that 78 percent of researchers believe competition for funding has intensified in the past decade, and 63 percent say it directly influences the kinds of projects they choose to pursue.
I’ve been off writing for a while dealing with a lot of rejection and clearly I use this medium as an outlet for my feelings so today we’re going to talk about competition in science. And we’re going to try and go old school and find a lot of stats and studies, it’s going to be insanely nerdy. Like, more nerdy than it’s been in a really long time.
I have a mixed and confused relationship with competition. I have distinct memories of my first day on my MSc course, chatting to a fellow student who was here on a Rhodes scholarship from the US. The topic of healthcare came up and he said he didn’t think there could be any progress in healthcare in the UK because there was no competition. He said if you knew the doctor down the road was charging slightly less and getting more people, you wanted to win those people to your practice so you did that by becoming a better doctor and innovating.
In the academic sphere, David Helfand has been an outspoken critic of the tenure system in the States. His opinion is that it breeds complacency. That there is a degree to which you put no effort in once you have tenure because there is no ‘need’ for you to do so. He turned down tenure early in his career and insisted instead on reviews by his peers every five years. Other critical faculty suggest he has tenure in spirit, if not in name, so this is all for show.
One of Helfand’s arguments is that tenure stifles academic freedom. I struggled with this at first and had to sit with the concept for sometime before it crystalised in my brain, and it’s mostly because he’s talking about aggregate academic freedom, not necessarily individual academic freedom. Academic freedom should be the right to pursue whatever research you want in whatever field. But Helfand’s point is that the current system doesn’t deliver that collective freedom: it concentrates security in the hands of a few and leaves everyone else more constrained. Tenure is very costly to Universities so they get around this cost by hiring more non-tenured staff, who take on the brunt of the teaching and research. And then what happens is that these staff feel incumbent pressure to produce results quickly which often means twisting their story to fit fashions.
Kevin Gross and Carl Bergstrom have written extensively on this and their papers are excellent and involve much math I do not understand. Irrespective, I shall try to summarise for you. In a system where ‘publish or perish’ is the state of play, most researchers will opt for projects which produce results in a timely and sensible manner. But when competition within that system intensifies, the ‘safe’ options become crowded spaces.
To stand out, it is more important to move towards riskier projects because the chances of a researcher standing out in that space are higher.
And this is where we get into field evolution theory. I told you it was going to be nerdy. Singh et al published a paper in 2022 where they used more complicated math to quantify the rise and fall of scientific fields. They show that when a field starts, it is small and often fairly interdisciplinary. Topics become ‘hot’ through a variety of social processes. Someone might publish a paper that is particularly good and impactful (note I said impactful, not high impact). That paper is picked up by a large lab and they run with the topic. This increases prestige. There is a ton of literature out there on how larger labs and larger institutions carry more ‘weight’ when it comes to publishing in a field.
A 2022 survey by Nature found that 78 percent of researchers believe competition for funding has intensified in the past decade, and 63 percent say it directly influences the kinds of projects they choose to pursue.
So, if a big lab at a prestigious institution picks up a topic there is more widespread belief in its validity. This is where Morgan and their colleagues come in with their paper on how Prestige drives epistemic inequality in the diffusion of scientific ideas. Which basically shows that when an idea comes from a high-status lab or university, it diffuses faster and more broadly across the scientific network than similar ideas from lower-prestige institutions. The authors call this effect epistemic inequality, it’s a kind of social amplification that makes certain ideas sort of “go viral” independent of intrinsic merit.
On an individual level, this is called the Matthew Effect. First established by Merton in 1968, Wikipedia (donate if you can) sums it up as ‘the rich get richer and the poor get poorer’. From a scientific point of view a number of studies have shown this to be true. Ideas from big-name labs, irrespective of their quality, garner more attention. Often simply because we’re lazy. We use cognitive shortcuts. ‘Well Fred said it worked like this so that must be how it is’.
And at this point our idea (or field), according to Singh, has reached maturity. And mature fields sustain their visibility through scale, not conceptual breakthroughs. Let’s take the era of big data as an example.
Around 10-15 years ago, the technologies for measuring things like genes and proteins and metabolites was new and exciting and evolving. Almost every year some new machine turned up which could measure things better and faster and cheaper. Now we have plateaued. To bring in a film title, we can now measure almost everything, everywhere all at once. Which means we’ve moved out of the ‘discovery’ phase and into the ‘mature’ phase. Real discovery is no longer a new type of data, but rather more of the same data but in many, many different contexts.
And that shift has created a weird asymmetry. We can generate data at a pace that vastly outstrips our capacity to make sense of it. And this problem is driven as much by competition as by technology. When research careers depend on grants and metrics, it is safer to promise scale than insight; funding panels reward quantity as a proxy for signal productivity. ‘I have made lots of data, this is good’. The race to stay visible fuels ever-larger datasets and collaborations, even when the conceptual and real-world impact pay-offs are thin. And we can see this with ever increasing funding going to large consortium projects like the Human Cell Atlas and projects using UK Biobank data.
Don’t get me wrong, discovery research is important. But at some point we’re going to have maxed out what we can measure and we’re going to have to start actually finding out what all these new receptors and new druggable targets and new genes actually do. And at that point, competition may have priced out all the people who can do hypothesis-driven research in favour of tons of people who are really good at using R. We’ve become extraordinarily good at data generation and integration, but progress in theory, modelling, and causal inference hasn’t kept pace. If we bring it back to Singh’s paper, big-data biology is a field at peak maturity, whether it declines or renews itself will depend on whether we can make interpretation, rather than accumulation, the next frontier.
Funding panels and hiring committees are currently rewarding the visible productivity of big datasets and multi-author consortia.
Actual data interpretation, which requires more experiment and less data analysis, looks ‘unproductive’. In a market where jobs and money are scarce, it’s almost safer to produce more data than to ask harder questions. The result is a feedback loop of gloom in which competition drives conformity.
For early career researchers, joining these large groups doing big data work offers security and visibility, without the need for a high-risk strategy. Junior researchers experience competition as existential; senior researchers experience it as strategic. For early career scientists, the next grant, paper, or contract determines whether they stay in the system at all, so they optimise for what is measurable and immediate. For senior academics, competition becomes a game they no longer have to play at full risk. With tenure and reputations already secured, they compete from a position of privilege where they lead the consortia, they review the grants, they decide which questions are fundable and which aren’t. In shaping the criteria for success, they also shape the behaviour of everyone trying to reach it.
And because many of those senior scientists sit on funding boards, the short-term incentives they grew up with become the next generation’s operating system. Most UK grants now run for three to five years a timescale that makes genuine risk-taking almost impossible. You can collect data and generate deliverables in that window, but you can’t easily build a new theory or test a complex causal model. The system funds activity rather than understanding, so even good researchers behave rationally within irrational constraints. Short funding cycles make science look productive while slowly eroding its actual capacity for long-term insight.
If we bring this all back round to where we started, with Gross and Bergstrom, the authors suggest that funding agencies rewarding novelty or rapid output, pushes scientists towards headline-grabbing work. And with funding bodies receiving record numbers of applications, competition is higher than it’s ever been before. And because they are such eloquent authors, I will leave the consequences of that to Gross and Bergstrom, who said that “Changes in the intensity of competition for various scientific positions and accolades do not merely have screening effects on the labour force; they end up restructuring the type of work that is conducted. Risk profiles shift, and with those shifts come cultural changes in what is considered valuable work— particularly for those researchers at career stages where they cannot choose to opt out of the race.”
Dr Yvonne Couch
Dr Yvonne Couch is an Associate Professor of Neuroimmunology at the University of Oxford. Yvonne studies the role of extracellular vesicles and their role in changing the function of the vasculature after stroke, aiming to discover why the prevalence of dementia after stroke is three times higher than the average. It is her passion for problem solving and love of science that drives her, in advancing our knowledge of disease. Yvonne shares her opinions, talks about science and explores different careers topics in her monthly blogs – she does a great job of narrating too.