Étude franco-canadienne du principe légaliste : le processus qualitatif et interprétatif du texte pénal
Bibliographic record
Abstract
A true pillar in the field of criminal law, the Rule of Law principle a. k. a nullum crimen nulla poena sine lege implies that a person is not to be criminally sentenced pursuant to a specific text within the Law. Does such principle apply in Canada as it does in France ? Does it rise from the same foundations and does it carry the same weight ? Such are the questions the author tries to address in the current study. To that effect, main convergences and divergences in both Canadian and French Criminal Law are bought forward to further oppose two common occurrences of these identities so one will reflect, in a comparative and critical manner, upon them in terms of their significance -both theoretical and pragmatic. Moreover, the first part of this study focusses on the Rule of Law principle as it overtakes the legislator, i. e. in the manner with which the Law demands quality while, in the second part, the emphasis lies on how the judge applies the principle in the context of the interpretation of the repressive text.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.018 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.012 | 0.036 |
| Scholarly communication | 0.021 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".