The Scientifc Workforce Policy Debate: Do We Produce too Many Biomedical Trainees?
Bibliographic record
Abstract
The number of trainees that the biomedical research enterprise produces currently far exceeds the number of academic PI positions available. Although calls for increased funding of research are routine, relatively few in the scientifc community have asked the question, Are we producing too many trainees? Here, I review current thinking on this issue in the literature and summarize the arguments for both the yes and no sides. Some have argued that the surplus of trainees acts as a deterrent for talented individuals considering a career in research, and that radical structural reforms to the current system are needed to uncouple training and work. Others have countered that competition is an essential feature of the biomedical research engine, and that the solution lies in legitimizing non-academic alternative career paths for scientists. This is a debate that requires the scientifc community to re-examine fundamental questions regarding the nature of PhD and postdoctoral training.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.054 | 0.068 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.030 |
| Scholarly communication | 0.013 | 0.018 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.018 | 0.012 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".