The Phraseology of Legal French and Legal Popularisation in France and Canada: A Corpus-Assisted Analysis
Bibliographic record
Abstract
The popularisation of legal knowledge is a critical issue for equal access to law and justice. Legal discourse has been justly criticised for its obscure terminology and convoluted phrasing, which notably led to the Plain Language Movement in English-speaking countries. In Canada, the concept of Plain Language has been applied to French since the 1980s due to the official policy of bilingualism, while the concept has only been recently discussed in France. In this paper, we examine the impact of Plain Language rewriting on legal phraseology in French popularisation contexts. The first aim of our study is to see if plain texts published in France contain more traces of legal phraseology than French Canadian texts. Our second objective is to determine if a ‘phraseology of plain language’ can be identified across genres and languages. To do this, we compare two corpora of expert-to-expert legal texts written in French—made up, respectively, of legislative texts published in France and judicial texts published by the Supreme Court of Canada—with two corpora of texts that are claimed to have been written in Plain French Language for a non-expert readership—texts that guide laypersons through legal and administrative processes in France and summaries of decisions by the Supreme Court of Canada. Using n-grams, we extract and discuss the patterns that emerge from the corpora. In particular, our analyses rely on the concept of ‘lexico–grammatical patterns’, defined as the minimal unit of meaningful text made up of recurrent sequences of lexical and grammatical items. We then identify a sample of recurring lexico–grammatical patterns and their discursive functions.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.012 | 0.019 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".