But Names Won't Necessarily Hurt Me: Considering the Effect of Disparaging Statements on Reputation
Bibliographic record
Abstract
The author proposes a change in how some courts apply the test for defamatory meaning — a change that in her view would help to protect freedom of expression without compromising the protection of reputation or altering the substantive law of defamation.To be defamatory, a statement must tend to harm reputation. However, Canadian case law shows that disparaging statements are often assumed to be defamatory, even when they may have little potential to harm reputation because a right-thinking audience member is unlikely to believe them. The author argues that this is the result of an overly literal approach to ordinary meaning, a disregard for how right-thinking people interpret statements, and a tradition of not adducing evidence of context to prove meaning. Social science evidence shows that a variety of factors — from pre-publication knowledge and opinions to the form in which the words were expressed — can substantially alter an audience’s interpretation of a statement. The approach proposed by the author would require courts to place more emphasis on the entire context of an impugned statement in determining whether the statement would lower a right-thinking person’s estimation of the plaintiff. Although leading more evidence of context would add a degree of complexity, it would not place an undue burden on the parties. Any loss of efficiency would be justified, given the importance of freedom of expression and the fact that the aim of defamation law is to protect reputation, not feelings.
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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 | 0.012 | 0.077 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.015 |
| Scholarly communication | 0.012 | 0.012 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.007 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 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".