Success in academia is as much about grit as talent
economist.com
economist.com
I think this might be simply because a career setback event crates selection pressure in the months following it, purging the bad candidates from this pool. As a result, this purged pool then outperforms the no-setback pool for simple statistical reasons.
I see no reason to conclude that the individual scientists’ behavior was changed by the event.
Therefore I don’t follow the conclusion cited above.
And it’s also not clear if the people who drop out from the setback pool are necessarily the non-gritty people, or maybe the not-so-smart people.
Therefore I don’t follow how they come to the conclusion about grit vs aptitude cited in the headline.
> By focusing on grant proposals that fell just below and just above the funding threshold, we compare “near-miss” with “near-win” individuals to examine longer-term career outcomes.
This is an example of a 'regression discontinuity analysis'. The underlying assumption is that applications just-above and just-below the NIH funding threshold have roughly the same merit, and falling on either side of the threshold can be explained by noise in the review process. So they're explicitly comparing scientists whose applications are equal with respect to merit - meaning the people dropped were not 'bad candidates' and the people funded were not 'better candidates'.
Since candidates are (assumed to be) equal in terms of merit, comparing the eventual outcomes of the two groups is a way to infer the causal effect of early funding or rejection on career outcome.
My point is that the simple event of a near-miss creates "evolutionary pressure" in the aftermath of the event, removing weaker candidates from the pool (their professors fire them, ...) and thereby biases that pool for success.
For example, suppose you have a lot of non-NIH resources (startup, foundation grants, etc). You might be more comfortable submitting an unpolished grant, knowing that while it would be great to have it funded on the first round, you can survive a while longer if you have to.
On the other hand, the lab which burns all of its startup funding to push one grant over the payline is in a weaker position, Especially if that grant doesn’t open up new directions for the renewals.