Collaboration, Stars, and the Changing Organization of Science: Evidence from Evolutionary Biology
Bibliographic record
Abstract
We report a puzzling pair of facts concerning the organization of science. The concentration of research output is declining at the department level but increasing at the individual level. For example, in evolutionary biology, over the period 1980 to 2000, the fraction of citation-weighted publications produced by the top 20% of departments falls from approximately 75% to 60% but over the same period rises for the top 20% of individual scientists from 70% to 80%. We speculate that this may be due to changing patterns of collaboration, perhaps caused by the rising burden of knowledge and the falling cost of communication, both of which increase the returns to collaboration. Indeed, we report evidence that the propensity to collaborate is rising over time. Furthermore, the nature of collaboration is also changing. For example, the geographic distance as well as the difference in institution rank between collaborators is increasing over time. Moreover, the relative size of the pool of potential distant collaborators for star versus non-star scientists is rising over time. We develop a simple model based on star advantage in terms of the opportunities for collaboration that provides a unified explanation for these facts. Finally, considering the effect of individual location decisions of stars on the overall distribution of human capital, we speculate on the efficiency of the emerging distribution of scientific activity, given the localized externalities generated by stars on the one hand and the increasing returns to distant collaboration on the other.
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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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".