Bibliographic record
Abstract
Formal attribution provides a means of recognizing scientific contributions as well as allocating scientific credit.This paper examines the processes by which attribution arises and its interaction with market assessments of the relative contributions of members of scientific teams and communities -a topic of interest organizational economics of science and in understanding scientific labor markets.We demonstrate that a pioneer or senior scientist's decision to co-author with a follower or junior scientist depends critically on market attributions as well as the timing of the co-authoring decision.This results in multiple equilibrium outcomes each with different implications for expected quality of research projects.However, we demonstrate that the Pareto efficient organisational regime is for the follower researcher to be granted co-authorship contingent on their own performance without any earlier precommitment to formal attribution.We then compare this with the alternative for the pioneer of publishing their contribution and being rewarded through citations to back to it.While in some equilibria (especially where co-authoring commitments are possible) there is no advantage to interim publication, in others this can increase expected research quality.
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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 | 0.027 | 0.102 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.012 | 0.015 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.008 | 0.005 |
| Insufficient payload (model declined to judge) | 0.031 | 0.004 |
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".