Bibliographic record
Abstract
The interdisciplinary temperament of “Law and the Humanities” is both perplexing for law and intriguing for the humanities. This perplexity and this intrigue come to a head precisely over one of the most important institutional necessities and problems of law: judgment. If a text is not a truth but a debate; if it embodies not one story or meaning but many; if a statute, let us say, or a court case cannot be neatly separated from literature, or rhetoric, or politics – then there is literature, and rhetoric, and politics, in every interpretation and in every decision. A philosophical treatise can be subversive, open-ended, and speculative; a literary reading probably should be. A judge, however, must decide what this text means, whether this statute applies, who wins, who loses, and even sometimes, who lives and who dies. One of the central questions that the influence of the humanities on law raises is this: how, and with what legitimacy, can judgment take place if the texts on which judges base their decision do not – even in principle, let alone in practice – yield “one right answer.” The question of judgment becomes then a serious problem. It is a problem for positivists, of course, who entirely reject this approach to interpretation and meaning. It is no less a problem for scholars of the humanities in law, who have to try to find an answer to it if they wish to be relevant to legal institutions at all.
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 | 0.019 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.010 | 0.098 |
| Scholarly communication | 0.020 | 0.018 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.008 | 0.011 |
| Insufficient payload (model declined to judge) | 0.005 | 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".