Beetles: Biased Promotions and Persistence of False Belief
Bibliographic record
Abstract
This paper develops a theory of promotion based on evaluations by the already promoted. The already promoted show some favoritism toward candidates for promotion with similar beliefs, just as beetles are more prone to eat the eggs of other species. With such egg-eating bias, false beliefs may not be eliminated by the promotion system. Our main application is to scientific revolutions: when tenured scientists show favoritism toward candidates for tenure with similar beliefs, science may not converge to the true paradigm. We extend the statistical concept of power to science: the power of the tenure test is the probability (absent any bias) of denying tenure to a scientist who adheres to the false paradigm, just as the power of any statistical test is the probability of rejecting a false null hypothesis. The power of the tenure test depends on the norms regarding the appropriate criteria to use in promotion and the empirical evidence available to apply these criteria. We find that the scientific fields at risk of being captured by false paradigms are those with low power. Another application is to hierarchical organizations: eggeating bias can result in the capture of the top of organizations by the wrong-minded.
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 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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".