Bibliographic record
Abstract
The issues raised by the Crown on appeal in Morgentaler v. The Queen from the acquittal of the accused were rendered moot when the Supreme Court of Canada declared the abortion statute (section 251 of the Criminal Code) to be unconstitutional. There was no need for the Court to discuss either the issue of the use of the "defence of necessity" or defence counsel's "bad law" argument. Nevertheless, Chief Justice Dickson found the "bad law" argument of defence counsel, Morris Manning, Q.C., "so troubling," he felt "compelled to comment" on it. Mr. Manning argued that, although the jury was to take its instructions in the law from the judge, it had a right not to apply the law in the case to the facts because the abortion statute was "bad law." In his decision, Chief Justice Dickson reiterated that it is the duty of the judge to instruct the jury in the law and the function of the jury to apply the facts to the law, and that Mr. Manning was wrong to tell the jury otherwise. Among other things, the Chief Justice used a "racist jury" example to demonstrate Mr. Manning's error. The author argues in this comment that the Chief Justice's example was ill-conceived and inapposite, and concludes that the jury and Mr. Manning should be commended for helping to rid Canada of an oppressive abortion law.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.014 | 0.009 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.030 | 0.021 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".