Chasing Reputation: The Argument for Differential Treatment of Public Figures in Canadian Defamation Law
Bibliographic record
Abstract
When comparing the seminal Supreme Court of Canada defamation decisions of the 1990s and 2000s, it is apparent that the Court's view on the importance of protecting reputation has changed. Recent decisions hail the importance of using freedom of expression as a countervailing interest against the oft-criticized strictures of the common law of defamation. Fundamental alterations in the nature of mass and interactive media and in the nature of reputation are two phenomena informing this change. Increased attention to the theorizing of "reputation," the interest whose protection animates the entire tort of defamation, reveals that reputation is itself a highly constructed, contextual, and malleable artifact. This article proposes recasting the tort of defamation into two different tracks: one for public figures, who pose the highest risk of abusing the tort, and one for private plaintiffs, whose reputational interest is akin to traditional notions of reputation.
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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 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.023 | 0.025 |
| Scholarly communication | 0.012 | 0.006 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.009 | 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".