MétaCan
Menu
Back to cohort
Record W1563553261 · doi:10.1017/cbo9780511617393

The Gender of Constitutional Jurisprudence

2004· book· en· W1563553261 on OpenAlexaff
Beverley Baines, Isabel Karpin, Martha I. Morgan, Alda Facio, Éric Millard, Blanca Rodríguez Ruiz, Martha C. Nussbaum, Ran Hirschl, Saras Jagwanth, Ruth Rubio-Marín, Hilal Elver, Reva Siegel

Bibliographic record

VenueCambridge University Press eBooks · 2004
Typebook
Languageen
FieldSocial Sciences
TopicJudicial and Constitutional Studies
Canadian institutionsQueen's University
Fundersnot available
KeywordsJurisprudenceAdjudicationConstitutionPolitical scienceLawConstitutional lawDemocracyState (computer science)Constitutional courtPolitics

Abstract

fetched live from OpenAlex

To explain how constitutions shape and are shaped by women's lives, the contributors to this volume examine constitutional cases pertaining to women in twelve countries. Analyzing jurisprudence about reproductive, sexual, familial, socio-economic, and democratic rights, they focus constructively on women's claims to equality, asking who makes these claims, what constitutional rights inform them, how they have evolved, what arguments work in defending them, and how they relate to other national issues. Their findings reveal significant similarities in outcomes and in reasoning about women's constitutional rights in these twelve countries, challenging the tradition of distinguishing constitutional jurisprudence depending on whether the country has a written or unwritten constitution, subscribes to civil or common law, is a federal or unitary state, limits constitutional adjudication to the public domain, accords international norms binding or subject to incorporation force, or relies on a specialized or general court to adjudicate constitutional matters.

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)0.011
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.010
Threshold uncertainty score0.048

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.011
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0070.061
Scholarly communication0.0100.007
Open science0.0010.004
Research integrity0.0020.005
Insufficient payload (model declined to judge)0.0050.001

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 head0.035
GPT teacher head0.247
Teacher spread0.211 · 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 designTheoretical or conceptual
Domainnot available
GenreOther

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

Citations39
Published2004
Admission routes1
Has abstractyes

Explore more

Same venueCambridge University Press eBooksSame topicJudicial and Constitutional StudiesFrench-language works237,207