Enriching the concept of vulnerability in research ethics: An integrative and functional account
Bibliographic record
Abstract
The concept of vulnerability is widely used in research ethics to signal attention to participants who require special protections in research. However, this concept is vague and under-theorized. There is also growing concern that the dominant categorical approach to vulnerability (as exemplified by research ethics regulations and guidelines delineating vulnerable groups) is ethically problematic because of its assumptions about groups of people and is, in fact, not very guiding. An agreed-upon strategy is to move from categorical towards analytical approaches (focused on analyzing types and sources of vulnerability) to vulnerability. Beyond this agreement, however, scholars have been advancing competing accounts of vulnerability without consensus about its appropriate operationalization in research ethics. Based on previous debates, we propose that a comprehensive account of vulnerability for research ethics must include four components: definition, normative justifications, application, and implications. Concluding that no existing accounts integrate these components in a functional (i.e., practically applicable) manner, we propose an integrative and functional account of vulnerability inspired by pragmatist theory and enriched by bioethics literature. Using an example of research on deep brain stimulation for treatment-resistant depression, we illustrate how the integrative-functional account can guide the analysis of vulnerability in research within a pragmatist, evidence-based approach to research ethics. While ultimately there are concerns to be addressed in existing research ethics guidelines on vulnerability, the integrative-functional account can serve as an analytic tool to help researchers, research ethics boards, and other relevant actors fill in the gaps in the current landscape of research ethics governance.
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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 | 0.065 | 0.042 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.009 | 0.005 |
| Science and technology studies | 0.008 | 0.131 |
| Scholarly communication | 0.015 | 0.038 |
| Open science | 0.003 | 0.016 |
| Research integrity | 0.008 | 0.010 |
| 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".