Hey Pablo—thanks for working this up. It’s nice to have some baseline estimates!
As you say, Tregellas et al. shows that the probability of tenure varies a lot with the number of first author publications. It would be interesting to know if tenure can be predicted better with other factors like one’s institution or h-index—I could imagine such a model performing much better than the baseline.
Two other queries:
I feel like we’re talking about tenure, rather than tenure track?
When you say things like “my personal estimate of the baseline probability of getting a permanent (tenured) position in academia should be with 90% probability between 10-30%”, it might be clearer to say you’re 90% sure that 10-30% of students get tenure? Otherwise I don’t know how to interpret this probability of a probability.
You’re right Ryan, I’ll modify the second complicated sentence. I am actually not sure what is the difference between tenure and tenure track, to tell the truth.
However, in one of the documents above I saw that institution is not such a strong predictor (point 4), but h index seemed useful (in point 2 the h-index is discussed).
Interesting. The point 2 article by van Dijk seems decent. Figure 1B says that the impact factor of journals, volume of publications, and cites/h-index are all fairly predictive. University rank gets some independent weighting (among 38 features, as shown in their supplementary Table S1), but not much.
Looks like although the web version has gone offline, the source code of their model is still online!
I strongly agree with Ryan that success is to a relatively large degree predictable, as can be done in the PCA decomposition of point 2 above, figure 1C. I think it would be very valuable to have such a model, but the current code is only for biology (the impact factor will fail for instance for anything different). If one wanted to fit a model to predict it, it could probably use google scholar and arxiv, but the trickiest part would be to recover the position of those people (the target), which may partially be done using google scholar.
Hey Pablo—thanks for working this up. It’s nice to have some baseline estimates!
As you say, Tregellas et al. shows that the probability of tenure varies a lot with the number of first author publications. It would be interesting to know if tenure can be predicted better with other factors like one’s institution or h-index—I could imagine such a model performing much better than the baseline.
Two other queries:
I feel like we’re talking about tenure, rather than tenure track?
When you say things like “my personal estimate of the baseline probability of getting a permanent (tenured) position in academia should be with 90% probability between 10-30%”, it might be clearer to say you’re 90% sure that 10-30% of students get tenure? Otherwise I don’t know how to interpret this probability of a probability.
You’re right Ryan, I’ll modify the second complicated sentence. I am actually not sure what is the difference between tenure and tenure track, to tell the truth.
However, in one of the documents above I saw that institution is not such a strong predictor (point 4), but h index seemed useful (in point 2 the h-index is discussed).
Interesting. The point 2 article by van Dijk seems decent. Figure 1B says that the impact factor of journals, volume of publications, and cites/h-index are all fairly predictive. University rank gets some independent weighting (among 38 features, as shown in their supplementary Table S1), but not much.
Looks like although the web version has gone offline, the source code of their model is still online!
I strongly agree with Ryan that success is to a relatively large degree predictable, as can be done in the PCA decomposition of point 2 above, figure 1C.
I think it would be very valuable to have such a model, but the current code is only for biology (the impact factor will fail for instance for anything different). If one wanted to fit a model to predict it, it could probably use google scholar and arxiv, but the trickiest part would be to recover the position of those people (the target), which may partially be done using google scholar.
I just posted another article I found on average publication rates in Norway for different positions, ages, fields and gender.