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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.018 | 0.085 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".