Codification of French legal terminology in France and Quebec: Comparative Analysis
Bibliographic record
Abstract
This thesis focuses on the comparative analysis of French legal terminology codification in France and Quebec. The thesis consists of five chapters and first establishes the conceptual foundations of language and legal terminology codification, then places them in the historical, sociopolitical, and legislative context of France and Quebec. This is followed by a presentation of the institutions responsible for the codification of legal terminology in the Francophone areas studied, their organizational hierarchy, and the codification processes, methods, and tools used in both contexts. Furthermore, the thesis offers a comparative view of the extralinguistic factors influencing codification, the institutional arrangements of both systems, and the individual stages of the codification processes. The final chapter includes a quantitative analysis of the results of terminological activities in France and Quebec in 2010-2024 and provides a critical assessment of the strengths and weaknesses of both approaches. The thesis also proposes specific recommendations for improving codification processes in the two environments studied.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".