Bibliographic record
Abstract
Justice Bastarache made a number of positive contributions to equality law, in respect of both the Canadian Charter of Rights and Freedoms and human rights legislation. This paper considers his contribution in light of a the ory that judges do at least two things when implementing a constitutional provision. They determine its meaning and the n they develop constitutional doctrine that they think best achieves it. Justice Bastarache came to the Court after it had determined the meaning of the equality right. He devoted his efforts to developing doctrine that aimed at putting it to work. Perhaps his keenest interest was in ensuring that the law coincided with the realities of the claimants and current beneficiaries of the law. He also adapted his highly contextualized section 1 analysis to the section 15 context. His work also shows a sensitivity to the collective interests at play in section 15 cases. In R. v. Kapp, while agreeing with the modifications of the Law v. Canada standard made by the majority of the Court, he presented an almost full the orized analysis of section 25, finding an Aboriginal right that answered the section 15 claim in that case. He also wrote reasons for judgment advancing the jurisdictional and substantive scope of human rights legislation.
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 | 0.014 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.021 | 0.031 |
| Scholarly communication | 0.012 | 0.005 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.012 | 0.015 |
| Insufficient payload (model declined to judge) | 0.003 | 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".