Should Society Allow Research Ethics Boards to Be Run As For-Profit Enterprises?
Bibliographic record
Abstract
BACKGROUND TO THE DEBATE: An important mechanism for protecting human research participants is the prior approval of a clinical study by a research ethics board, known in the United States as an institutional review board (IRB). Traditionally, IRBs have been run by volunteer committees of scientists and clinicians working in the academic medical centers where the studies they review are being carried out. However, for-profit organizations are increasingly being hired to conduct ethics reviews. Proponents of for-profit IRBs argue that these IRBs are just as capable as academic IRBs at providing high-quality ethics reviews. Critics argue that for-profit IRBs have a conflict of interest because they generate their income from clients who have a direct financial interest in obtaining approval.
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 | 0.115 | 0.189 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.010 | 0.043 |
| Scholarly communication | 0.028 | 0.031 |
| Open science | 0.005 | 0.011 |
| Research integrity | 0.058 | 0.043 |
| Insufficient payload (model declined to judge) | 0.009 | 0.004 |
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".