Bibliographic record
Abstract
Traditional legal language The English language of today is still recognisably the language of Chaucer and Shakespeare, of Abraham Lincoln and Winston Churchill, of the Book of Common Prayer and the Authorised Version of the Bible. It is also the language of lawyers in many countries: the United Kingdom, the United States, Canada, Australia, New Zealand and India, to name but a few. In English, lawyers draft documents and compose letters; in English, lawyers formulate statutes and propagate regulations; in English, lawyers prepare pleadings and argue their cases. Legal English, however, has traditionally been a special variety of English. Mysterious in form and expression, it is larded with law Latin and Norman French, heavily dependent on the past, and unashamedly archaic. Antiquated words flourish, such as aforementioned, herein, therein, whereas – words now rarely heard in everyday language. Habitual jargon and stilted formalism conjure a spurious sense of precision: the said, aforesaid, the same . Oddities abound: oath-swearers do not believe something, they verily believe it; parties do not wish something, they are desirous of it; the clearest photocopy only purports to be a copy; and so on. All this – and much more – from a profession that regards itself as learned. Some infelicities of expression, some overlooked nuances, some grammatical slips, can be forgiven. Lawyers are only human, and in the day-to-day practice of law they face an overwhelming weight of words.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.388 | 0.181 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".