IRBs and the Protection-Inclusion Dilemma: Finding a Balance
Bibliographic record
Abstract
Institutional review boards, tasked with facilitating ethical research, are often pulled in competing directions. In what we call the protection-inclusion dilemma, we acknowledge the tensions IRBs face in aiming to both protect potential research participants from harm and include under-represented populations in research. In this manuscript, we examine the history of protectionism that has dominated research ethics oversight in the United States, as well as two responses to such protectionism: inclusion initiatives and critiques of the term vulnerability. We look at what we know about IRB decision-making in relation to protecting and including "vulnerable" groups in research and examine the lack of regulatory guidance related to this dilemma, which encourages protection over inclusion within IRB practice. Finally, we offer recommendations related to how IRBs might strike a better balance between inclusion and protection in research ethics oversight.
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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 | 0.685 | 0.707 |
| Meta-epidemiology (narrow) | 0.002 | 0.004 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.035 | 0.122 |
| Scholarly communication | 0.051 | 0.050 |
| Open science | 0.011 | 0.040 |
| Research integrity | 0.061 | 0.071 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".