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Record W3046943377 · doi:10.1080/1554477x.2020.1783932

Writing Gender Out or Working It Back In? Media Coverage of Child Benefits in Canada

2020· article· en· W3046943377 on OpenAlexaffabout
Rebecca Wallace, Elizabeth Goodyear‐Grant

Bibliographic record

VenueJournal of Women Politics & Policy · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicGender Politics and Representation
Canadian institutionsQueen's University
Fundersnot available
KeywordsNewspaperFraming (construction)Gender equalityPolitical scienceMedia coverageConversationVariation (astronomy)Gender studiesPrint mediaPublic relationsSociologyMedia studiesGeographyLaw

Abstract

fetched live from OpenAlex

This article analyzes the (de)gendering of child benefits in Canadian print media, using an automated analysis of coverage of two governments’ child benefits policies. The analysis finds that gender and women’s equality are deprioritized in the framing of the policies’ beneficiaries and goals, even when the party in power shifts from the right wing of the spectrum to center-left. There is some variation in coverage across newspapers and across journalists. Notably, women journalists’ coverage of child benefits policies is more attentive to gender and women’s equality, suggesting that they may serve as agents of change in bringing gender back into the national conversation.

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)0.016
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 score0.044
Threshold uncertainty score0.319

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.016
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0110.016
Science and technology studies0.0060.002
Scholarly communication0.0060.001
Open science0.0010.002
Research integrity0.0010.001
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 head0.073
GPT teacher head0.325
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

Citations3
Published2020
Admission routes2
Has abstractyes

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