The (Changing) Knowledge Production Function: Evidence from the MIT Department of Biology for 1970-2000
Bibliographic record
Abstract
Considerable attention has been focused, in recent years, on the role that graduate and postdoc students play in the production of academic knowledge.Using data from the MIT Department of Biology for the period 1970-2000, we analyze the evolution over time of four fundamental aspects of their productivity: i) training duration; ii) time to a first publication; iii) productivity over the training period; and iv) collaboration with other scientists.We identified four main trends that are common to graduate students and postdocs.First, training periods have increased for later cohorts of graduate and postdoc students.Second, later cohorts tend to publish their initial first-author article later than the earlier cohorts.Third, they produce fewer first-author publications.Finally, collaborations with other scientists, as measured by the number of coauthors on a paper, have increased.This increase is driven by collaborations with scientists external to a trainee's laboratory.We interpret these results in light of the following two paradigms: the increased burden of knowledge that later generations of scientists face and the limited availability of permanent academic positions.
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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 | 0.003 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".