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Record W2784246330 · doi:10.3138/utlj.2017-0073

Gender identity, gender pronouns, and freedom of expression: Bill C-16 and the traction of specious legal claims

2018· article· en· W2784246330 on OpenAlexaffvenueabout
Brenda Cossman

Bibliographic record

VenueUniversity of Toronto Law Journal · 2018
Typearticle
Languageen
FieldComputer Science
TopicHate Speech and Cyberbullying Detection
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsOpposition (politics)LawPolitical sciencePoliticsLegislatureHuman rightsLegislationJurisdictionSociologyBinary oppositionFreedom of expressionEpistemologyPhilosophy

Abstract

fetched live from OpenAlex

Bill C-16, An Act to Amend the Canadian Human Rights Code and the Criminal Code was a government bill intended to provide equal protection of the law to trans and gender non-binary Canadians. It protects individuals from discrimination within the sphere of federal jurisdiction, as well as protecting against hate propaganda and hate crimes, on the basis of gender identity and gender expression. The opposition to previous legislative attempts to protect trans rights focused on questions of sex-segregated spaces such as public bathrooms. In the course of the debate over Bill C-16, however, a new discourse of opposition emerged: Bill C-16 was said to be a fundamental threat to freedom of expression. This article argues that this claim lacks validity, yet it gained remarkable traction. The article traces the shifting opposition discourse and argues that freedom of expression provided a new and legitimizing discourse for long-standing conservative opposition to trans rights. Finally, it seeks to explain the traction of the specious legal claims, contextualizing them within existing public discourses of political correctness and freedom of expression under attack.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.014
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.721
Threshold uncertainty score0.555

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.014
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0160.082
Scholarly communication0.0130.007
Open science0.0010.005
Research integrity0.0040.005
Insufficient payload (model declined to judge)0.0030.000

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 head0.012
GPT teacher head0.209
Teacher spread0.197 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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

Citations23
Published2018
Admission routes3
Has abstractyes

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Same venueUniversity of Toronto Law JournalSame topicHate Speech and Cyberbullying DetectionFrench-language works237,207