Bibliographic record
Abstract
This article, detailing the implications of “ethics drift” for critical work in the academy, reports on an ethics challenge to a non-research-based scholarly text. It analyzes how General Research Ethics Boards (GREBs) can threaten academic freedom when they lack a clear definition of “human subject” research, fail to distinguish between empirical research using humans and scholarly engagement of important social/political issues within human contexts, and overstep the limits of their jurisdiction when they agree to arbitrate on scholarship that ought to be resolved through open debated rather than administrative mechanisms. The article emphasizes that in public democratic institutions, those who contribute to decisions and policies, whether through formal process or by informal tacit ideology, are acting not as individuals but as functionaries of the institution and must bear public accountability and its attendant critiques. The article ends with a recommendation for arms-length oversight of the workings of GREBs.
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 | 0.028 | 0.028 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.007 | 0.061 |
| Scholarly communication | 0.016 | 0.021 |
| Open science | 0.004 | 0.012 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.020 | 0.003 |
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".