Reconceptualizing Stars: Scientist Helpfulness and Peer Performance
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
It is surprising that the prevailing performance taxonomy for scientists (star versus nonstar) focuses only on individual output and ignores social behavior, because innovation is often characterized as a communal process. To develop a deeper understanding of the mechanisms by which scientists influence the productivity of others, I expand the traditional taxonomy of scientists that focuses solely on productivity and add a second, social dimension: helpfulness to others. Using a combination of academic paper publications and citations to capture scientist productivity and the receipt of academic paper acknowledgments to measure helpfulness, I examine the change in publishing output of the coauthors of 149 scientists that die. Coauthors of highly helpful scientists that die experience a decrease in output quality but not output quantity. Meanwhile, the deaths of high productivity scientists that are not highly helpful do not influence their coauthors' output. In addition, scientists who are helpful with conceptual feedback (critique and advice) have a larger impact on the performance of their coauthors than scientists who provide help with material access, scientific tools, or technical work. Within the context of evaluating scientific productivity, it may be time to update our conceptualization of a “star.” This paper was accepted by Lee Fleming, entrepreneurship and innovation.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.058 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.029 | 0.138 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it