Debating Sharia: Islam, Gender Politics, and Family Law Arbitration
Bibliographic record
Abstract
Table of Contents Introduction: Situating the Debate Foreword: Situating the Debate within Others in European and American Contexts Introduction - Situating the Debate in Ontario Part I. Practicing Religious Divorce among North-American Muslims 1. Practicing an 'Islamic Imagination': Islamic Divorce in North America 2. Faith-Based Arbitration or Religious Divorce: What was the Issue? Part II. Regulating Faith-Based Arbitration 3. Multiculturalism Meets Privatisation: The Case of Faith-Based Arbitration 4. 'Sharia' Courts in Canada: A Delayed Opportunity for the Indigenization of Islamic Legal Rulings. Part III. Defining Islamic Law in the West 5. Asking Questions About Sharia: Lessons From Ontario. 6. Islamic Law and the Canadian Mosaic: Politics, Jurisprudence, and Multicultural Accommodation. Part IV. Negotiating the Politics of Sharia-Based Arbitration 7. 'The 'Good' Muslim/'Bad' Muslim Puzzle?: The Assertion of Muslim Women's Islamic Identity in the Sharia Debates. 8. 'The Muslims Have Ruined Our Party:' A Case Study of Ontario Media Portrayals of Supporters of Faith-Based Arbitration. Part V. Analyzing Discourses of Race, Gender, and Religion 9. 'Sharia in Canada?' Mapping Discourses of Race, Gender and Religious Difference. 10. Agency and Representations: Voices and Silences in the Ontario Sharia Debate Part VI. Managing Religion in the Canadian State 11. Managing the Mosaic: The Work of Form in 'Dispute Resolution in Family Law: Protecting Choice, Promoting Inclusion.' 12. Construing the Secular: Implications of the Ontario Sharia Debate Concluding Thoughts Conclusion: Debating Sharia in the West List of Contributors
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.011 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".