Bibliographic record
Abstract
There are puzzles in research-ethics review that confound researchers in the social sciences. First, how did the biomedical research ethics process capture the research-ethics perspectives in the social sciences? Second, why has it proved impossible to recognize the relevance of stepping outside the biomedical paradigm? Third, what institutional agencies have not permitted researchers in the social sciences to abandon the biomedical paradigm and adopt one that is relevant for social scientists? There are particular elements that characterize the ethics-review process. Some phrases as ‘rigor’ dissuade inductive researchers, the power of hierarchical nature of research-ethics committees inevitably impose the biomedical stance in research, the colonizing nature of research-ethics betrays its American bias, and the insistence that all researchers implement a data-management plan is not relevant to the social sciences. Some of the more common suggestions to improve the situation are unworkable.
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 | 0.618 | 0.727 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.003 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.018 | 0.098 |
| Scholarly communication | 0.035 | 0.065 |
| Open science | 0.008 | 0.017 |
| Research integrity | 0.033 | 0.047 |
| Insufficient payload (model declined to judge) | 0.004 | 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".