Research bystanders, justice, and the state: Reframing the debate on third‐party protections in health research
Bibliographic record
Abstract
Research participants are afforded protections to ensure their rights and welfare are not unduly jeopardized by research activities. Yet people who do not meet the criteria for research participant status may likewise be impacted by research activities, and ethicists argue that protections should be afforded these "research bystanders." The standard rationale for extending protections to research bystanders contends that they are sufficiently like research participants that the ethical principles governing health research ought to extend to them. In this article we argue that this analogical reasoning is mistaken. Salient moral differences mean that research ethics frameworks are not fit for purpose. We defend the research bystander category by articulating a novel foundation for this new class of stakeholder. Focusing on bystanders directly impacted by publicly funded health research, we argue that bystanders are sometimes owed protections-but neither because of their similarity to research participants nor because research ethics principles should extend to them. Instead, we reframe the issue as a question of justice. Building on the work of Douglas MacKay, we argue that bystanders to publicly funded health research are owed protections as citizens of liberal states to whom the state owes duties of justice. The state has duties to protect the interests of citizens and to conduct health research. When the means by which the state fulfils the latter duty comes into conflict with the means by which it fulfils the former, the state must ensure that those impacted, including research bystanders, are afforded protections.
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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 | 0.169 | 0.115 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.023 | 0.203 |
| Scholarly communication | 0.028 | 0.045 |
| Open science | 0.005 | 0.032 |
| Research integrity | 0.039 | 0.039 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".