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Record W2336403513 · doi:10.1017/s1743923x16000155

Julia Gillard and the Gender Wars

2016· article· en· W2336403513 on OpenAlexaff
Linda Trimble

Bibliographic record

VenuePolitics & Gender · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicGender, Feminism, and Media
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsPoliticsMetaphorNewspaperGender studiesPower (physics)AllegoryPolitical sciencePrime ministerSociologyLawLiteratureLinguisticsArtPhilosophy

Abstract

fetched live from OpenAlex

The Australian news media used the metaphor of the gender war(s) to describe Julia Gillard's political strategies and speech acts in the final nine months of her term as that nation's first woman prime minister. In particular, the metaphor was mobilized in response to Gillard's October 9, 2012, parliamentary speech on sexism and misogyny. Based on a critical discourse analysis of the gender wars allegory as it was applied to Gillard by three Australian newspapers, my article analyzes the meanings revealed by metaphoric constructions of the former prime minister's speeches as unusual and unjust forms of political warfare. I argue that the trope of the gender wars cast Gillard's political tactics as a violation of deeply held cultural norms about appropriate behavior on the so-called political battlefield, and it worked both to discipline Gillard for raising issues of sexism and gender inequality in politics and to bracket gendered power relations out of everyday understandings of political competition.

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)0.001
metaresearch head score (Gemma)0.003
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: Qualitative
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.017
Threshold uncertainty score0.034

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0080.009
Scholarly communication0.0050.003
Open science0.0000.003
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0030.001

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 head0.062
GPT teacher head0.322
Teacher spread0.260 · 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

Citations22
Published2016
Admission routes1
Has abstractyes

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