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Record W2875844369

Thirty Years with Section 15 of the “Charter”: A Report on Legislative Terminology in Canada

2018· article· en· W2875844369 on OpenAlexaboutno aff
Richard Haigh

Bibliographic record

VenueeYLS (Yale Law School) · 2018
Typearticle
Languageen
FieldArts and Humanities
Topiclinguistics and terminology studies
Canadian institutionsnot available
Fundersnot available
KeywordsCharterSection (typography)TerminologyLegislaturePolitical scienceLawHistoryComputer sciencePhilosophyLinguistics
DOInot available

Abstract

fetched live from OpenAlex

The article summarizes the results of an audit of every statute in Canada (other than Quebec) for evidence of discriminatory language that could be contrary to s. 15 of the Canadian Charter of Rights and Freedoms. It is a revisit of audits that were completed in the three-year holiday granted to s. 15 until April 1985, which generally concluded that governments did not systematically address the wide-ranging instances of direct discrimination in Canadian legislation as promised. Over thirty years later, the article shows that the problem still persists. Wide disparities in legislative attempts to comply with s. 15 remain — with the ground of “sex” remaining the most concerning. The article also reviews other grounds of direct discrimination that can be found in legislative provisions, although these are generally less problematic.\nIn terms of gendered language, Canadian laws now contain a random admixture of neutral and non-neutral language, some discriminatory and some likely justified. Piecemeal reform continues to be the norm. The audit reveals such continuing problems in statutes as gendered job titles, assumed male officials and professionals, outdated terms, archaic language in older statutes and incorporated laws. Relatively simple solutions, such as comprehensive neutral drafting reforms are examined. The article concludes with a call for comprehensive review and reform by federal, provincial and territorial governments.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.713
Threshold uncertainty score0.291

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 studies0.0000.001
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 head0.022
GPT teacher head0.225
Teacher spread0.204 · 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 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

Citations0
Published2018
Admission routes1
Has abstractyes

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