Daphne Barak Erez, Biblical Judgments: New Legal Readings in the Hebrew Bible
Bibliographic record
Abstract
Some years ago, I was invited to give a presentation on judicial independence to a group of judges from Mexico. I struggled to figure out how to connect with these visitors to Canada. We lacked a common history, legal system, language, or religion. They spoke Spanish, and my remarks would be translated from English; they were educated and worked in a civil law system, while I was from a common law system; they came from a country where nearly three-quarters of the population are Catholic, while I am Jewish. I then realized that, perhaps—just perhaps—the Old Testament could bridge our differences. I quoted this verse from Deuteronomy: “Do not pervert justice or show partiality. Do not take bribes, for bribes blind the eyes of the wise and subvert the cause of the just.”1 I realized I had the attention of the judges when I heard the translator say “Deuteronomio.” I saw sparks of recognition and familiarity on the faces of the Mexican judges. Recourse to the Bible had succeeded in traversing linguistic, cultural, legal, and religious differences.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.007 | 0.016 |
| Scholarly communication | 0.007 | 0.013 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.009 | 0.011 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".