Ethics and Confidentiality: Reflections and Lessons Learned Post-<i>Parent and Bruckert v R and Magnotta</i>
Bibliographic record
Abstract
Abstract In May 2012, a former research assistant contacted the Montréal police about an interview he had conducted with Luka Magnotta for the SSHRC-funded research projectSex Work and Intimacy: Escorts and their Clientsfour years previously. That call ultimately resulted in theParent and Bruckert v R and Magnottacase. Now, a decade later, we are positioned to reflect on the collective lessons learned (and lost) from the case. In this paper, we provide a lay of the Canadian confidentiality landscape before teasing out ten lessons fromParent c R.To do so, we draw on personal archives, survey results from sixty researchers, twelve key informant interviews with qualitative sociolegal and criminology researchers, and documentary analysis of university research policies. The lessons, which range from the clichéd, to the practical, to the frustrating, have implications for the individual work of Canadian researchers and for the collective work of academic institutions.
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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 | 0.095 | 0.092 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.067 | 0.086 |
| Scholarly communication | 0.021 | 0.013 |
| Open science | 0.006 | 0.011 |
| Research integrity | 0.011 | 0.026 |
| 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".