Bibliographic record
Abstract
I want to discuss the very recent decision of the Supreme Court of Canada in the Torstar case. The New Zealand leading case in this area, the Lange case, was significantly influenced by the Canadian Charter and by the contemporaneous development of human rights jurisprudence in a number of jurisdictions. Now it seems the New Zealand jurisprudence has played a significant part in this recent development of Canadian defamation law. This important decision has opened up the law of defamation for media in Canada. It also demonstrates nicely how common law systems of law are part of a robust process of fertilisation and cross-fertilisation of ideas, analysis and experience. The Supreme Court used a comparative analysis to reach its decision, by looking at developments elsewhere, including New Zealand. And in turn, this decision could influence where our law goes in the future.
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 | 0.006 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.015 | 0.015 |
| Scholarly communication | 0.013 | 0.006 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.018 | 0.015 |
| Insufficient payload (model declined to judge) | 0.012 | 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".