Bibliographic record
Abstract
The present study deals with two particular aspects of the problem of retrospective operation of criminal statutes. The principle of the retrospective operation of a procedural statute, as will be seen first, bears some exceptions. S. 36 of the Interpretation Act in particular prescribes that the retrospective operation of procedural enactments takes place only to the extent that the new rules of procedure may be adapted to proceedings taken before the new rules came into effect (R. v. Mustapha Ali). Yet, as will be seen subsequently, it is often very difficult to distinguish between an enactment of substantive nature and one of procedural nature in order to give a retrospective operation to the latter only. This difficulty arose particularity after the modifications of the Criminal Code concerning the offence of driving a motor vehicle with more than a specified proportion of alcohol in the blood (R. v. Johnston). After a brief discussion of those two cases, the author suggests that it is the duty of the legislator to indicate in clear terms what the retrospective operation of a new statute will be, in order to avoid many problems of interpretation.
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 | 0.017 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.009 |
| Scholarly communication | 0.012 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".