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Record W2804938249 · doi:10.1177/1365712718782990

Gender discrimination and juries in the 20th century

2018· article· en· W2804938249 on OpenAlexaboutno aff
Andrew L‐T Choo, Jill Hunter

Bibliographic record

VenueThe International Journal of Evidence & Proof · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicJury Decision Making Processes
Canadian institutionsnot available
Fundersnot available
KeywordsJuryPolitical sciencePoliticsFranchiseLawInequalitySociologyJury selection

Abstract

fetched live from OpenAlex

This article presents a comparative study of the 20th-century exclusion of women from participation on juries. It explains that until the 1970s, and in some cases even the 1990s, substantial formal limitations on jury franchise were placed on women in Ireland, Canada, the United States, New Zealand and Australia. This situation existed notwithstanding women’s equality of political franchise through the vote and despite judicial references to the centrality of the jury. While in England and Wales women were not treated differently from men in formal terms after the 1920s, property qualifications denied them substantive equality and informal limitations excluded women disproportionately. We highlight some distinctive features of the English experience as compared and contrasted with the laws and policies on jury composition operating in other jurisdictions, and ask whether the legacies left by the traditionally unrepresentative jury and the battles for gender equality offer lessons relevant to understanding jury trials in contemporary times.

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)0.022
metaresearch head score (Gemma)0.030
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.032
Threshold uncertainty score0.118

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0220.030
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0190.036
Scholarly communication0.0080.005
Open science0.0010.008
Research integrity0.0040.005
Insufficient payload (model declined to judge)0.0060.001

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 head0.194
GPT teacher head0.446
Teacher spread0.252 · 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 designQualitative
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

Citations7
Published2018
Admission routes1
Has abstractyes

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