In the (Canadian) Shadow of Islamic Law: Translating Mahr as a Bargaining Endowment
Bibliographic record
Abstract
This article addresses the dilemmas of Muslim women living in Canada as they negotiate between the constitutional and juridical systems of the dominant society, on the one hand, and the Muslim community, on the other. It will examine the ideological assumptions about law and multiculturalism that have worked to depoliticize the stakes of law in Marion Boyd's report, Protecting Choice, Promoting Inclusion. With the Islamic institution of mahr in the background, this article suggests a methodology to evaluate the costs and benefits of abstract legal rules as they are actually used by the parties in the "shadow of the law" to acquire something from the other party, make concessions, or simply put an end to the relationship. In offering a distributional narrative of legal pluralism, this article demonstrates that while legal orders produce group subjectivities in plural spaces, they also, and more importantly, distribute desires, interests, and bargaining endowments between individuals and groups in an unpredictable and often contradictory fashion.
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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 | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: yes | Theoretical or conceptual | low |
| gpt | no category Domain: not available · Genre: Other About the Canadian research system: no · About a Canadian topic: yes | Theoretical or conceptual | low |
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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.015 | 0.063 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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.
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".