A Corpus-Based Approach to Studies in Legal Phraseology: An Overview
Bibliographic record
Abstract
The language of the law as it is called “legalese” has very distinctive lexical and structural patterns which in many ways different from the “traditional forms of language”. Its conservatism is linked directly to the need for unambiguous language that has already been tried and tested in the courts. By retaining to traditional lexis and structure lawyers can be confident that the language of the law is consistent and precise. This study aims to give some insights on apparent lexico-grammatical features characterised legal phraseology. The present study adopts a corpus-based approach to investigate those distinctive features of legal phraseology such as the uses binomial words, colligation of prepositions, prefabricated word combinations directly prescribed by law, and their semantic functions. This overview compiles data from the books, and empirical studies as well as theoretical and conceptual works conducted in the premises of legal phraseology. Some implications for English for specific purposes are given.
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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 | 0.009 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.029 | 0.060 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".