Bibliographic record
Abstract
In the fall of 2018, The US National Science Foundation (NSF) implemented a new policy on sexual harassment. A few months later, the National Institutes of Health (NIH), took a further step in the fight against harassment by announcing that researchers accused of harassment, but not yet found guilty, could nonetheless be excluded from the lists of potential reviewers of submitted projects. We also observe a recent tendency to call for the retraction of published peer-reviewed results on the basis that their conclusions are considered to go against the moral convictions of some social groups, though the lack of validity of the results has not been proven. It is certainly a legitimate question to ask whether these kinds of policies and moral critiques, which directly link the practice of science to the moral behavior of the scientists in the larger society, do not initiate a profound transformation in the relations between science and society by adding to the usually implicit norms governing the scientific community a new form of moralization of the scientists themselves. We analyze these recent events in terms of a new process of moralization of science and ask whether these new rules of conduct may lead to doing better or more robust science.
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 | 0.050 | 0.112 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.075 |
| Scholarly communication | 0.016 | 0.014 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.012 | 0.018 |
| Insufficient payload (model declined to judge) | 0.006 | 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".