Sex Work Research, Ethics Review Processes, and Institutional Challenges for “Sensitive” Collaborative Research
Bibliographic record
Abstract
This article examines challenges and barriers seemingly endemic to the research ethics review process. We argue that these challenges and barriers disempower community stakeholders in sex work research and that they put our studies and those who consent to participate in them at risk. To advance this position, we interrogate three of our own encounters with research ethics boards (REBs) in the context of current scholarship on meaningful collaborative research and REB roles and responsibilities in relation to sex work and other sensitive research. As these encounters illustrate, there is an urgent need for established REB processes to be opened up to allow for and respect non-academic expertise. We suggest that such policy and process revisions are particularly important given the growing requirement for meaningful stakeholder involvement in all aspects of studies that engage marginalized groups. In this new anti-oppressive collaborative framework, stakeholder community expertise thus informs study development and design, as well as the collection and analysis of data, and decisions regarding where and how study findings are to be shared. Research ethics review processes must be revised accordingly to acknowledge and give due consideration to community-based expertise. We conclude by proposing institutional and community-based strategies for resisting and revising current research ethics review structures and processes. Applying the lens of whore stigma to select REB encounters, this article contributes to existing research about ethical and anti-oppressive sex work research methods and methodologies, arguing that we must account for REB encounters in the growing body of theory that seeks to understand and articulate how best to conduct sex work research in partnership with sex workers.
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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 | 0.590 | 0.560 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.034 | 0.089 |
| Scholarly communication | 0.040 | 0.030 |
| Open science | 0.007 | 0.035 |
| Research integrity | 0.013 | 0.022 |
| Insufficient payload (model declined to judge) | 0.003 | 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".