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Record W2064651397 · doi:10.1017/s0008423901777815

Feminists and the Courts: Measuring Success in Interest Group Litigation in Canada

2001· article· en· W2064651397 on OpenAlexaffabout
F. L. Morton, Avril Allen

Bibliographic record

VenueCanadian Journal of Political Science · 2001
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCorporate Law and Human Rights
Canadian institutionsUniversity of TorontoUniversity of Calgary
Fundersnot available
KeywordsCharterAppealStatus quoInterest groupScope (computer science)Political scienceAbortionLaw and economicsLawEconomics

Abstract

fetched live from OpenAlex

This study proposes a new model for assessing success in interest group litigation. The model is applied to 47 appeal court rulings concerning feminist issues in 21 cases involving the Canadian Charter of Rights and Freedoms and 26 non-Charter cases. The study operationalizes the concept of ''success'' by including not just outcome (''who wins''), but also the effect of the case on the ''policy status quo'' (PSQ) and the creation of favourable or unfavourable legal resources (precedents). Feminist claims prevailed in 72 per cent of the cases. The PSQ optic reveals that previous studies overstate the significance of feminist losses (13), since only three of these changed the PSQ in a direction opposed by feminists. There were 17 cases that changed the PSQ in a direction desired by feminists. Feminist litigation has been most successful in the policy areas of abortion, private-sector discrimination and pornography. Success has been lowest in the areas of sexual assault and income tax. These findings suggest that interest group litigation can achieve significant policy change and that the scope of policy studies should be expanded to include judge-made policy.

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)0.016
metaresearch head score (Gemma)0.079
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.082
Threshold uncertainty score0.593

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0160.079
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0130.012
Science and technology studies0.0150.010
Scholarly communication0.0080.003
Open science0.0040.007
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0040.000

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 head0.028
GPT teacher head0.210
Teacher spread0.181 · 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 designObservational
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

Citations46
Published2001
Admission routes2
Has abstractyes

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