Bibliographic record
Abstract
The aim of my comment on William Twining's recent book Globalisation and Legal Scholarship (2011) is to present and contrast two models of general (or universal) jurisprudence: the one favoured by Twining and the other adopted by Jeremy Bentham. Twining's model aims to be general by capturing the great variety of laws as they exist in the world; by contrast, Bentham argued that it is mostly prescriptive claims about law that can be universal. I argue that the descriptive model suffers from serious flaws: it either has to posit arbitrary boundaries between law and non-law (this is the problem from which HLA Hart's version of descriptive jurisprudence suffers) or it does away with all boundaries, resulting in a shapeless barrage of data (this is the problem with Twining's version of this model). By contrast, I argue, the Benthamite version of general jurisprudence is free from these problems. I then argue that Twining's descriptive approach leads him to various prescriptive recommendations, which I believe are unattractive.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".