Freedom of Expression and Humor in Canada: The Case of <i>Jérémy Gabriel</i> v <i>Mike Ward</i>
Bibliographic record
Abstract
Abstract Purpose: This chapter examines how two basic rights, freedom of expression, and the right to equality based on one’s dignity, reputation, and honor, were balanced in a case involving a stand-up comedian and an adolescent suffering from Treacher Collins syndrome. Methodology/Approach: The case is contrasted with Jürgen Habermas’ concept of the public sphere and with the intrinsic and utilitarian values that Canadian courts have attributed to free speech. Findings: Because the case was dealt with first in a human rights tribunal and then by a court of appeal, a number of considerations were overlooked in court proceedings: how laughter occurs; the broadening of Ward’s audience and its consequences; and Ward’s publicity strategy. These aspects are explored here to give a more complete picture of the case beyond the court decisions. Originality/Value: In Canada, freedom of expression is usually dealt with ordinary courts. A whole new avenue for dealing with this right is human rights bodies and tribunals. Contesting free speech in the name of defamation is being replaced by rights entrenched in human rights charters, such as the right to equality based on the preservation of one’s dignity, reputation, and honor.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.046 | 0.019 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.006 | 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".