MétaCan
Menu
Back to cohort
Record W1816871147

Negotiating the Contours of Unlawful Hate Speech: Regulation Under Provincial Human Rights Law in Canada

2005· article· en· W1816871147 on OpenAlexaboutno aff
Luke McNamara

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicHate Speech and Cyberbullying Detection
Canadian institutionsnot available
Fundersnot available
KeywordsLawLegislatureStatuteFree speechPolitical scienceHuman rightsNegotiation
DOInot available

Abstract

fetched live from OpenAlex

This article has examined more than half a century of operation of provincial and territorial hate speech laws in Canada. This examination has confirmed that free speech sensitivity has long been an integral and enduring feature of the administration and interpretation of legislative regimes for the regulation of hate speech—a finding that should come as a shock to no-one. What is surprising is the way in which free speech sensitivity has impacted on the operation of hate speech laws, and the effects of that influence on the quality of the protection provided to victims by existing provincial and territorial laws.... One of the chief objectives of hate speech prohibitions in provincial and territorial human rights statutes is to draw a line between free speech which must be protected (or at least tolerated), and hate speech which must be outlawed and sanctioned because of its harmful effects. Such line-drawing exercises are never simple and almost always controversial. However, the extent of the uncertainty and controversy has been exacerbated in Canada by the multi-layered influences of free speech sensitivity described above, as well as ongoing differences amongst decisionmakers regarding the legitimate scope of hate speech prohibitions. The net result is that the contours of unlawful hate speech in Canada are anything but sharp. On the contrary, the boundary between free speech and hate speech remains contested and fluid.

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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.913
Threshold uncertainty score0.249

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 head0.211
Teacher spread0.201 · 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
GenreEmpirical

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

Citations1
Published2005
Admission routes1
Has abstractyes

Explore more

Same topicHate Speech and Cyberbullying DetectionFrench-language works237,207