MétaCan
Menu
Back to cohort

Freedom of Expression and Humor in Canada: The Case of <i>Jérémy Gabriel</i> v <i>Mike Ward</i>

2021· book-chapter· en· W3156375715 on OpenAlexaboutno aff
Anne‐Marie Gingras

Bibliographic record

Venuenot available
Typebook-chapter
Languageen
FieldComputer Science
TopicHate Speech and Cyberbullying Detection
Canadian institutionsnot available
Fundersnot available
KeywordsDignityHuman rightsTribunalAppealHonorLawValue (mathematics)Political scienceSociology

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.781
Threshold uncertainty score0.598

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.009
GPT teacher head0.185
Teacher spread0.176 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreOther

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".

Quick stats

Citations0
Published2021
Admission routes1
Has abstractyes

Explore more

Same topicHate Speech and Cyberbullying DetectionFrench-language works237,207