Consistent churn of early career researchers: an analysis of turnover and replacement in the scientific workforce
Bibliographic record
Abstract
Scientific advancement depends on sustaining a workforce trained in the scientific method. These scientists can in turn train the next generation of scientists and ensure that novel research continues to contribute to society. However, recent evidence shows that academic careers are shortening over time (Milojević et al., 2018). This coupled with a surplus of early career researchers (ECRs) relative to academic faculty positions raises the concern that talented scientists might not be retained in academic research. However, the empirical properties of the modern academic workforce are largely unknown. We use the publication histories of 3.5 million researchers to examine how career age composition of the scientific workforce has changed over 30 years. We find that for most fields there has been a relatively stable distribution of scientists at various career stages, with notable exceptions in Health and Physics. Using these data, we calculate the rate that researchers enter and depart the academic workforce, finding the most rapid turnover among ECRs. While varying the rates of turnover and replacement based on these observations, we develop a model and project a continuation of current career age proportions. This implies that the makeup of the workforce may remain stable with an abundance of ECRs. We conclude that the disconnect between the number of ECRs and the number of available long-term academic positions may be attributed to the collaborative nature of the academy. The stabilization of the workforce with a high proportion of ECRs urges a reassessment of how academic careers are portrayed.
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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 | 0.008 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".