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Record W2811400418 · doi:10.4000/revdh.4109

Constitutional Treatment of Hate Speech and Freedom of Expression: a Canada – U.S. perspective

2018· article· en· W2811400418 on OpenAlexaboutno aff
Pyeng Hwa Kang

Bibliographic record

VenueRevue des droits de l’homme · 2018
Typearticle
Languageen
FieldComputer Science
TopicHate Speech and Cyberbullying Detection
Canadian institutionsnot available
Fundersnot available
KeywordsHarmMulticulturalismLawPolitical scienceConstitutional lawSupreme courtFreedom of expressionSociologyHuman rights

Abstract

fetched live from OpenAlex

The present article explores the constitutional treatment of hate speech in Canadian and American law vis-à-vis the paramount place freedom of expression occupies in both legal systems1. The author first pronounces on the conceptional divergence of the freedom, opining that American free speech has retained much of its status as a preferred freedom given its unique historical fomenting process and predilection toward a quasi-absolutist interpretation of the First Amendment. Canada, however, has explicitly declined to institute a hierarchical approach of rights, thus effectively creating a level-playing field through an egalitarian-driven perception of rights when they are in collision. The argument continues by looking into major jurisprudential developments of hate speech in the two respective constitutional orders. Identifying one of the principal legal basis for Canadian courts to strike down constitutional challenges raised in hate speech cases to be strongly grounded in the communitarian understanding of the harm inflicted by hate speech, the observation hints at the distinctively Canadian legal attitude’s overture toward special group rights, multiculturalism, or grosso modo – the promotion of pluralism. The American courts, however, have been reluctant in suppressing speech activity by confiding in a set of extremely narrowly tailored tests to justify constitutional infringements of free speech.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.812
Threshold uncertainty score0.967

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 head0.017
GPT teacher head0.238
Teacher spread0.221 · 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 designBench or experimental
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
Published2018
Admission routes1
Has abstractyes

Explore more

Same venueRevue des droits de l’hommeSame topicHate Speech and Cyberbullying DetectionFrench-language works237,207