Bibliographic record
Abstract
Much academic work continues to operate within the cramping and pervasive spirit of a black-letter mentality that encourages scholars and jurists to maintain legal study as an inward-looking and self-contained discipline. There is still a marked tendency to treat law as somehow a world of its own that is separate from the society within which it operates and purports to serve. This is a disheartening and disabling state of affairs. Accordingly, this article will offer both a critique of the present situation and suggest an alternative way of proceeding. The writer recommends a shift from philosophy to democracy so that legal academics will be less obsessed with abstraction and formalism and more concerned with relevance and practicality. In contrast to the hubristic and occasionally mystical aspirations of mainstream scholars, it presents a more humble depiction of the worth and efficacy of the jurisprudential and scholarly project in which ‘usefulness’ is given pride of place. Of course, these fundamental charges are not applicable to all legal scholars. Many scholars are engaged in work that not only challenges the prevailing paradigm of legal scholarship, but also explores exciting new directions for legal study. It will be part of the essay to acknowledge those contributions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.015 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.029 | 0.056 |
| Scholarly communication | 0.023 | 0.019 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.012 | 0.014 |
| Insufficient payload (model declined to judge) | 0.008 | 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".