Every Moment Counts: The Legal Value of Time in Canadian Criminal Law and Procedure
Bibliographic record
Abstract
Time plays a significant role in the life of the law. Drawing on the work of Elizabeth F. Cohen regarding the political value of time, this article examines the legal value of time. Focusing on Canadian criminal law and procedure, the article illustrates how time is tightly woven into the fabric of various constitutional, statutory and common law norms. As examples, time is fundamental to sentencing, in addition to determining significant issues such as the scope of criminal liability for serious crimes (e.g. first degree murder), whether an accused should be acquitted on the basis of a defence, or whether evidence should be admitted or excluded. This article explores two ways in which time is employed: scientifically measured durational time (drawing from Cohen’s work) and context-sensitive determined time.
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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 | 0.006 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.007 | 0.011 |
| Science and technology studies | 0.018 | 0.035 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".