Vexed Again: Social Scientists and the Revision of the Common Rule, 2011-2018
Bibliographic record
Abstract
In revising the Federal Policy for the Protection of Human Subjects (Common Rule) between 2009 and 2018, regulators devoted the vast bulk of their attention to debates over biomedical research. They lacked both expertise in and concern about the social sciences and humanities, yet they imposed their will on experts in those fields. The revision process was secretive, spasmodic, and unrepresentative, especially compared to rulemaking in Canada, where social scientists participate in the process, and revisions take place every few years. The result was a final rule that offers some wins for social science and the humanities, but that fails to solve the problems identified by Ezekiel Emanuel and in the 2011 advance notice of proposed rulemaking.
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 | 0.104 | 0.228 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.024 | 0.029 |
| Scholarly communication | 0.028 | 0.013 |
| Open science | 0.004 | 0.013 |
| Research integrity | 0.047 | 0.044 |
| Insufficient payload (model declined to judge) | 0.003 | 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".