Previous Convictions as a Consideration in Canadian Sentencing Decisions
Bibliographic record
Abstract
This thesis empirically examines the Canadian judicial consideration of previous convictions at the criminal sentencing stage. Further, it addresses the possible benefit of amending the current Criminal Code of Canada, section 727 with respect to how that section, which deals with previous convictions, is written when compared to other common-law jurisdictions. In Canada, an offender’s prior record is widely recognised as an aggravating factor that should be considered at sentencing. However, section 727 of the Criminal Code indicates only that judges may take prior record into account when determining an offender’s sentence. Ostensibly, the way in which previous convictions are handled in a sentencing determination in Canada appears to be less prescriptive than that of other common-law jurisdictions. The difference lies potentially within the use of the terms must and may: one indicates absoluteness; the other an afforded discretion. This thesis finds that for the majority of offence categories examined, where the offender was facing their third or greater like prior-conviction, the mean custodial sentence lengths were less than the first like-prior conviction sentence lengths overall. Importantly, across offence categories, it would not matter if offenders were facing their first conviction or their twentieth, the mean custodial sentence lengths lacked any significant difference. These empirical findings suggest that previous convictions do not influence sentence length. This thesis establishes that there is no real benefit in amending our Criminal Code. It is theorized that the choice to use the term “must” or “may” may only hold symbolic meaning rather than have any pragmatic application. It is proposed that previous convictions should be excluded as an aggravating factor in the determination of sentence quantum. Lastly, the thesis calls into question the current Canadian utilitarian approach to sentencing offenders and suggests reform that aligns objectives with evidenced effectiveness.
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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 | 0.005 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.011 | 0.003 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".