That makes sense, thanks for the explanation! Yeah still a bit confused why they chose different numbers of years for the scientist and PhD, how those particular numbers arise, and why they’re so different (I’m assuming it’s 1 year of scientist funding or 5 years of PhD funding).
Yup, wanted to confirm here the ~100x in efficacy comes from getting 10x in relevance and 10x in ability (from selecting someone 10x better than the average research scientist).
Regarding the relative value of PhD vs scientist: the model currently values the average scientist at ~10x the average PhD at graduation (which seem broadly consistent with the selectivity of becoming a scientist and likely underrepresents the research impact as measured by citations—the average scientist likely has more than 10x the citation count as the average PhD). Then, the 5 years includes the PhD growing significantly as they gain more research experience, so the earlier years will not be as productive as their final year.
I’m confused where these assumptions are stored. All of the parameter files I see in GitHub have all of the `ability_at_*` variables set equal to one. And when I print out the average of `qa.mean_ability_piecewise` for all the models that also appears to be one. Where is the 10x coming from?
That makes sense, thanks for the explanation! Yeah still a bit confused why they chose different numbers of years for the scientist and PhD, how those particular numbers arise, and why they’re so different (I’m assuming it’s 1 year of scientist funding or 5 years of PhD funding).
Yup, wanted to confirm here the ~100x in efficacy comes from getting 10x in relevance and 10x in ability (from selecting someone 10x better than the average research scientist).
Regarding the relative value of PhD vs scientist: the model currently values the average scientist at ~10x the average PhD at graduation (which seem broadly consistent with the selectivity of becoming a scientist and likely underrepresents the research impact as measured by citations—the average scientist likely has more than 10x the citation count as the average PhD). Then, the 5 years includes the PhD growing significantly as they gain more research experience, so the earlier years will not be as productive as their final year.
I’m confused where these assumptions are stored. All of the parameter files I see in GitHub have all of the `ability_at_*` variables set equal to one. And when I print out the average of `qa.mean_ability_piecewise` for all the models that also appears to be one. Where is the 10x coming from?