Ultimate Legality: Reading the Community of Law
Bibliographic record
Abstract
Abstract This article is a contribution to the occasional series dealing with a major book that has influenced the author. Previous contributors include Stewart Macaulay, John Griffith, William Twining, Carol Harlow, Geoffrey Bindman, Harry Arthurs, André‐Jean Arnaud, Alan Hunt, Michael Adler, Lawrence O. Gostin, John P. Heinz, Roger Brownsword, Roger Cotterrell, Nicola Lacey, Carol J. Greenhouse, and David Garland. An initial twist: several acute observers would consider the way I read to be the most influential effect of reading on me – a way of reading that extends beyond the specificity of the text yet, in so doing, connects integrally with it. Salvation of specificity is at hand, however. That way of reading is intimately reflective of Derridean deconstruction and a hugely influential reading becomes his ‘Force of Law’. A problem ensues. Other influential reading came before my love of Derrida – influential reading to do with law and society (of course), with decolonization and imperialism, with engaged anthropology, and with critical legal studies. A retrospective revelation then follows. Derridean deconstruction is found to haunt and inform these other readings. They can be read in a way that inherently anticipates deconstruction. Some culminating coherence is offered by the inescapable insistence of community and the mutually intrinsic fusion of community and law.
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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 | 0.006 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.016 | 0.038 |
| Scholarly communication | 0.019 | 0.017 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".