Bibliographic record
Abstract
Classifying research proposals by risk of harm is fundamental to the approval process and the most pivotal risk category in most regulations is that of "minimal risk." If studies have no more than a minimal risk, for example, a nearly worldwide consensus exists that review boards may sometimes: (1) expedite review, (2) waive or modify some or all elements of informed consent, or (3) enroll vulnerable subjects including healthy children, incapacitated persons and prisoners even if studies do not hold out direct benefits to them. The moral and social purposes behind this threshold are discussed along with relevant views from the National Commission, NBAC, NHRPAC, Grimes v. Kennedy Krieger Institute, The Nuremberg Code, and The WMA's Declaration of Helsinki. Representative policies from Australia, Canada, South Africa, the U.S., and CIOMS are reviewed revealing different understandings of this sorting threshold. Six of nine frequently cited interpretations of "minimal risk" are untenable. The "absolute" interpretation of the "routine examination" standard is defended as best.
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 | 0.231 | 0.240 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.004 | 0.051 |
| Scholarly communication | 0.015 | 0.017 |
| Open science | 0.005 | 0.011 |
| Research integrity | 0.016 | 0.027 |
| Insufficient payload (model declined to judge) | 0.002 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".