Avoiding Risk, Protecting the “Vulnerable”: A Story of Performative Ethics and Community Research Relationships
Bibliographic record
Abstract
In February 2019, OUTSaskatoon, a 2SLGBTQ+ resource centre in Saskatoon, SK, received 1.1 M in federal funds to support a five-year project set to intervene in the instances and societal perpetuation of gender-based violence toward the 2SLGBTQ+ community. The project involved partnerships between OUTSaskatoon and the University of Saskatchewan, including a comprehensive research and evaluation stream to accompany the delivery of front-line services and educational activities. During the project’s application to the University’s Research Ethics Board (REB), members of the ethics review committee expressed heightened levels of fear and discomfort not only with the subject-matter, but with the role (and centrality) of the community organization within the research process. The documented experience explores pressing barriers to effective and ethical community-university research partnerships. To this end, the authors explore their communications with the REB alongside the themes of “vulnerability,” “risk-aversion,” and more broadly regarding the timelines of community work versus university processes. Together these themes maintain a culture of academic exceptionalism that causes significant barriers to the development of reciprocal partnerships between community partners and universities. In this case, the outcome was hopeful, as a formal complaint to the REB received a documented apology. In documenting this specific, though not unique, experience, we aim to highlight the possibilities for leaning in and building ethical space between and through community and academic environments to foreground both needed critique and collaborative pathways forward.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | MetaresearchResearch integrity Domain: Methods · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Qualitative | medium |
| gpt | MetaresearchScience and technology studies Domain: Methods · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Qualitative | medium |
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 1.000 | 0.999 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.997 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.999 |
| Insufficient payload (model declined to judge) | 0.000 | 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".