Teaching Legal English for Company Law: A Guide to Specialism and ELP Teaching Practices and Reference Books
Bibliographic record
Abstract
This article discusses one of the less mainstream areas of ESP teaching, that of legal English for students of company law. The author begins by analysing the approach used by subject-domain specialists themselves and the current criticism regarding the conservative textbook approach which continues to dominate teaching theory in this area. To this effect, she presents the results of a study carried out from October 2014 to March 2015 regarding subject-domain textbooks most used in Law Schools in Australia, Britain, Canada and the USA. The paper then addresses the question of teaching legal English to students of company law. After a brief outline of the three main theories underlying language teaching –behaviourist, cognitive and communicative– the author provides a critical guide to the main course books available to teachers in this rarefied area of specialised language learning, listing the types of exercises proposed, and evoking their overall strengths and weaknesses. To conclude, she suggests means of supplementing course book material.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.036 | 0.021 |
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".