Bibliographic record
Abstract
The criminal law has been criticized for failing to engage with the right to equality when delineating its permissible scope. While these criticisms are forceful, they must also be tempered by the structure of judicial review. The Supreme Court’s recent decision in R. v.Sharma is illustrative. Despite an avid dissent, a narrow majority found that Parliament’s decision to amend a prior sentencing law that conferred a benefit to a minority group could not by itself sustain a violation of the right to equality. This approach is principled as any other interpretation would undermine the constitutional framework for punishment under the Canadian Charter of Rights and Freedoms. This follows as the essence of the constitutional challenge in Sharma concerned whether refusing to permit a conditional sentence order for select offences would result in an unconstitutional punishment. By finding a violation of the right to equality, the minority circumvented the gross disproportionality standard required to declare a punishment unconstitutional under section 12 of the Charter. In its place, the minority would have imposed a mere proportionality standard under section1 for any punishment laws that retract a previously granted benefit to a minority group. Such an approach might be justifiable if there were no other means to consider equality interests when determining the constitutionality of sentencing laws. However, that is not the case as the reasonable hypothetical offender analysis under section 12 can ensure that the equality considerations implicit in the criminal law are given due weight. While conducting the analysis under section 12 does not change the result in Sharma, it upholds the principle underlying that provision requiring the scope of sentencing policy to remain reasonably broad to account for differing political opinions about the appropriate use of punishment.
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 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".