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Record W181468827

Women's Political Education: Developing Political Leadership in Canada and India

2009· article· en· W181468827 on OpenAlexaboutno aff
Catherine McGregor, Darlene E. Clover, Martha Farrell, Saswati Battacharya

Bibliographic record

VenueGlobal media journal Australia · 2009
Typearticle
Languageen
FieldSocial Sciences
TopicGender Politics and Representation
Canadian institutionsnot available
Fundersnot available
KeywordsPoliticsGender studiesGeneral partnershipSociologyNarrativeMetaphorPolitical sciencePublic relationsEconomic growthPublic administrationLaw
DOInot available

Abstract

fetched live from OpenAlex

This article reports on a recently completed study of women who are involved in formal and informal political roles in Canada and India (2008-2009). Our study is a partnership between the University of Victoria and the Society for Participatory Research in Asia. The intersections between feminist forms of adult education and the learning needs of women in political leadership in India and Canada are explored. The educational needs of each group are categorized and narratives analyzed to illustrate the complexity of the discourses that act to shape women’s political leadership identities and practices. We consider the similarities and differences between the countries, noting the persistence of gender based norms and expectations in both democracies and how these act as barriers to women’s participation in political life. Emerging from the idea of a politics of presence (Puwar, 2004), we offer political cross-dressing as a metaphor for feminist adult education practices that will enable a break through the civic ceiling women encounter in political spheres.

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)0.002
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.078
Threshold uncertainty score0.566

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.006
Science and technology studies0.0380.008
Scholarly communication0.0080.001
Open science0.0020.006
Research integrity0.0010.004
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 head0.145
GPT teacher head0.363
Teacher spread0.219 · 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 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

Citations1
Published2009
Admission routes1
Has abstractyes

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