Duty to disclose what? Querying the putative obligation to return research results to participants
Bibliographic record
Abstract
Many research ethics guidelines now oblige researchers to offer research participants the results of research in which they participated. This practice is intended to uphold respect for persons and ensure that participants are not treated as mere means to an end. Yet some scholars have begun to question a generalised duty to disclose research results, highlighting the potential harms arising from disclosure and questioning the ethical justification for a duty to disclose, especially with respect to individual results. In support of this view, we argue that current rationales for a duty of disclosure do not form an adequate basis for an ethical imperative. We review policy guidance and scholarly commentary regarding the duty to communicate the results of biomedical, epidemiological and genetic research to research participants and show that there is wide variation in opinion regarding what should be disclosed and under what circumstance. Moreover, we argue that there is fundamental confusion about the notion of "research results," specifically regarding three core concepts: the distinction between aggregate and individual results, amongst different types of research, and across different degrees of result veracity. Even where policy guidance and scholarly commentary have been most forceful in support of an ethical imperative to disclose research results, ambiguity regarding what is to be disclosed confounds ethical action.
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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 | 0.290 | 0.493 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.060 |
| Scholarly communication | 0.013 | 0.028 |
| Open science | 0.005 | 0.013 |
| Research integrity | 0.031 | 0.020 |
| Insufficient payload (model declined to judge) | 0.006 | 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; 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".