Bibliographic record
Abstract
Defamatory statements of fact published in mass media give rise to a legal problem of particular difficulty. When a defamatory statement of fact is published by mass media, the breadth of the statement’s dissemination is likely to maximize the harm to the person defamed. Yet in recent decades there has been an increasing consciousness among legislators and the judiciary of the importance of freedom of expression in democratic societies. Defamation cases are free speech cases in microcosm. Judicial appreciation of the important values at stake on both sides of cases involving defamatory statements of fact in mass media has led to recognition that the publication of such statements, when they relate to subjects of legitimate public interest, should in some circumstances be legally protected. As a result, Canadian law as to the availability of a defence of privilege for mass media has been in a state of evolution for many years. This article surveys the history of that evolution, which has led to a restatement of libel law in terms of free expression the ory. A cornerstone of that restatement is the recent recognition by the Supreme Court, in Grant v. Torstar, of a new defence of responsible communication on matters of public interest.
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 | 0.016 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.048 |
| Scholarly communication | 0.014 | 0.010 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.022 | 0.016 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".