The Right to Report, the Duty to Report, and the Costs of Reporting: A Framework for Answering Ethical Dilemmas for Qualitative Sociolegal Researchers
Bibliographic record
Abstract
Abstract It is well understood that safeguarding confidentiality is paramount to ensuring the success of research and the protection of participations. However, professional responsibilities and ethics of care can, at times, manifest in a requirement, or even a desire, to breach promises of confidentiality. We unpack this tension by drawing on research on the concepts of legal privilege, the right to report and the duty to report, the impacts of disclosures, as well as a study conducted with socio-legal and criminological researchers and criminalized or socially sanctioned communities who have participated in qualitative a research project. Our findings illustrate that while researchers with a clinical designation (e.g., nurses and social workers), enjoy clarity on professional duties, for researchers without such professional policies (e.g., criminologists) there is little guidance on when and how disclosures could and should take place. That said, when faced with the potential of actual and imminent threats of harm to an identifiable person, most researchers align with Supreme Court of Canada guidance, and would consider reporting, but with much deliberation regarding what constitutes harm, to whom one should report, and the consequences of disclosures. In the interests of contributing to this conversation, we conclude this paper with a decision-making framework that puts the Wigmore test into conversation with the Supreme Court of Canada’s 1999 Smith v Jones decision.
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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 | 0.466 | 0.311 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.013 | 0.009 |
| Science and technology studies | 0.036 | 0.211 |
| Scholarly communication | 0.039 | 0.034 |
| Open science | 0.010 | 0.021 |
| Research integrity | 0.017 | 0.015 |
| 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".