Sincere but Naive: Methodological Queries Concerning the British Columbia Polygamy Reference Trial
Bibliographic record
Abstract
Academics frequently serve as expert witnesses in legal cases, yet their role as transmitters of social scientific knowledge remains under‐examined. The present study analyzes the deployment of social science within British Columbia's polygamy reference trial where research is used to support the assertion that polygamy is inherently harmful to society. Within the trial record and the written decision, the protection of monogamy as an institution is performed in part through the marginalization of qualitative methodology and the concurrent privileging of quantitative studies that purportedly demonstrate widespread social harms associated with the practice of polygyny. Les universitaires servent souvent témoins experts dans les cas juridiques, mais leur rôle comme les transmetteurs de connaissance en sciences sociale restent sous‐examinés. La présente étude analyse le déploiement de sciences sociale dans l'essai de référence de polygamie de la Columbia britannique où la recherche est utilisée pour soutenir l'assertion que la polygamie est naturellement malfaisante pour la société. Dans le record de procès et la décision écrite la protection de monogamie comme une institution est exécutée partiellement par la marginalisation de méthodologie qualitative et le privilégier d'études quantitatives qui démontrent que la polygamie est intrinsèquement néfastes pour la société.
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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 | 0.588 | 0.738 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.013 |
| Science and technology studies | 0.027 | 0.054 |
| Scholarly communication | 0.025 | 0.011 |
| Open science | 0.012 | 0.012 |
| Research integrity | 0.015 | 0.017 |
| Insufficient payload (model declined to judge) | 0.004 | 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; 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".