"A Delicate Necessity": 'Bruker v. Marcovitz' and the Problem of Jewish Divorce
Bibliographic record
Abstract
Even hard cases can make good law — when courts ask themselves the right questions. Stephanie Bruker's case against ex-husband Jason Marcovitz was hard on two counts. Hard because, at its loftiest, one might say that it pits two religious freedoms against each other: a man's freedom not to grant a 'get' or Jewish divorce to his wife, and his wife's freedom to remarry according to the tenets of Judaism. Hard, too, because the wife's moral conduct was at odds with her stated beliefs - a not-infrequent fact of life, but one inconvenient for litigation. Overcoming the inconvenience and applying both domestic-law and comparative-law approaches, a 7:2 majority of the Supreme Court of Canada found for Bruker. In so doing, the Court reversed the Quebec Court of Appeal and upheld the trial judge's damages award against Marcovitz for not honouring an undertaking to appear before a rabbinical tribunal for the purpose of granting a 'get'.
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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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.022 | 0.024 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.017 | 0.013 |
| Insufficient payload (model declined to judge) | 0.004 | 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".