Bibliographic record
Abstract
The field of mixed legal studies has recently been engaged in discussions about the virtues of merit-driven versus integritydriven judgments in law making. Integrity-driven judgements aspire to locate solutions in a way that will generate greater coincidence with one overall legal tradition in settings where two or more are mixed. The latter style of judgement has historically preoccupied mixed jurisdictions where the Civil law has felt the need to struggle for its place when paired with a dominating Common law alter. A more pragmatic merit-driven approach has been advocated for by some mixed jurisdiction scholars as a means of allowing the mixité to flourish according to its contemporary context and bundle of sociological needs. This paper reexamines the tension between these two approaches to judgement. It does so by taking a slightly off-kilter look at mixité in one of the more vexed and volatile mixed jurisdictions in the world: Israel/Palestine. The paper does not take as a point of inquiry either the classic Civil law/Common law mix of the jurisdiction, nor its increasingly more strident religious law/secular law tension. The paper, rather, focuses and elaborates upon one of the reigning metaphors of mixed legal studies – food – through the most prominent tension of the jurisdiction – Israeli/Palestinian – and pushes that metaphor in ways that might better reflect the dialogic and contested nature of all mixités.
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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 | 0.008 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.011 | 0.042 |
| Scholarly communication | 0.017 | 0.013 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 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".