Bibliographic record
Abstract
Whereas comparative law scholarship has traditionally focused on Westphalian legal systems, Patrick Glenn famously argued that the legal tradition offers a descriptively and normatively superior analytic focus point. Descriptively, the legal tradition model would better account for law’s prominent epistemic dimension. Normatively, it would steer clear of Western centrism and attendant validation of colonialism. This chapter aims to diffuse the apparent opposition between legal system and legal tradition by offering an account of the legal system as (intellectually self-determined) tradition that is nonetheless consistent with the Westphalian conception. In particular, it is contended that an internal investigation of the kind advocated by Glenn (and others) yields an overall picture of legal systems as very much shaped like bee swarms. The relevant characteristic of bee swarms for present purposes is their projecting elusive, fuzzy edges around a comparatively well-defined centre of gravity, namely, their respective queen bees. I argue that legal systems, as internally delineated epistemic communities, likewise boast a well-defined institutional grounding (Part I) encircled by fluid edges (Part II).
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.019 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.014 | 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".