Bibliographic record
Abstract
This chapter explicates, explores, and commends Patrick Glenn’s choice to recognize and emphasize the significance of tradition, his master concept for understanding law, in the workings of all legal orders. However, it does not share the evangelical enthusiasm that Glenn suggests should flow from this recognition. That enthusiasm is based, I argue, on a quite idiosyncratic and contestable conception of what traditions ‘truly’ involve, absent contingency or corruption. Glenn believes that recognizing the traditionality of legal orders allows us to see them as open to greater mutual recognition, tolerance, conciliatory living together, than we commonly recognize when we speak in other terms, say, of legal systems, cultures, families, and so forth. Without his excessively sunny conception of the nature of tradition as its foundation, however, a lot of the ‘conciliatory’ hopefulness so winning in Glenn’s writings seems to rest on shifting and uncertain ground. We should acknowledge that law is typically founded on and in traditions, that complex legal orders indeed typically are traditions, simply because these are facts, and important ones. I fear, however, that such acknowledgment will of itself do little to advance the mutual accommodations among legal and social orders that Glenn admirably favours.
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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 | 0.004 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.727 | 0.547 |
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".