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Record W1953916609 · doi:10.60082/2563-8505.1072

The Political Impact of the Charter

2005· article· en· W1953916609 on OpenAlexaboutno aff
Judy Rebick

Bibliographic record

VenueSupreme Court law review · 2005
Typearticle
Languageen
FieldSocial Sciences
TopicMulticultural Socio-Legal Studies
Canadian institutionsnot available
Fundersnot available
KeywordsCharterVictoryPoliticsLawSection (typography)Political scienceGovernment (linguistics)Bill of rightsHuman rights

Abstract

fetched live from OpenAlex

Ms. Rebick discusses the history of section 15 and argues that it is important to note that it was the women’s movement and the burgeoning disability rights movement that fought to strengthen the language of section 15 against some considerable resistance on the part of the Liberal government of the day. The strong equality rights language of section 15 is due to the creativity, mobilization, and persistence of the women’s movement. Whatever the legal impact of the Charter the political impact has been overwhelmingly positive. Whether or not women have actually won rights under section 15, women believe that they have the right to equality and that is incredibly important. In fact, Canadians believe deeply in the equality rights of the Charter and this belief has helped to fuel equality rights movements in Canada that have been mobilizing over the last decades up to and including the most recent example of gays and lesbians in the same-sex marriage struggle. Ms. Rebick notes that the women’s movement considered the inclusion of section 28 a major victory for equality rights in Canada, our Equal Rights Amendment. Yet in the history of Charter litigation and despite the dismal record of litigation on gender equality for women, section 28 has rarely been used. Lawyers see section 28 as a back up to section 15 rather than as a counter to section 1 and perhaps some discussion on section 28 would be a useful way to improve the court record on women’s equality.

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)0.001
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: none
Teacher disagreement score0.950
Threshold uncertainty score0.821

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
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 head0.044
GPT teacher head0.375
Teacher spread0.330 · 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
Published2005
Admission routes1
Has abstractyes

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