The Barrie guide to the English legal system 2021-2022 (October 2021 update)
Bibliographic record
Abstract
Teaching resource for London Metropolitan University students on English law modules. This manual is designed to familiarise you with some basic ideas about the system of law in England and Wales. It is largely English law that you will be learning and applying in this course. The law of any country is much more than a “set of rules”. The rules are what we perceive on the surface, but creating and underpinning them is a "legal culture" or way of thinking. The way the rules work can only be understood by considering the context in which they are generated and applied. \n \nIf you have not studied or worked with English Law before, you will find it invaluable to gain a basic knowledge of legal structure, legal language, legal research and legal analysis, before looking at the substantive law which forms the main body of this course. \n \nThe guide has been updated to include Brexit content.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.510 | 0.442 |
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".