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Record W2317499032 · doi:10.1177/1940161213495455

Is It Personal? Gendered Mediation in Newspaper Coverage of Canadian National Party Leadership Contests, 1975–2012

2013· article· en· W2317499032 on OpenAlexaffabout
Linda Trimble, Angelia Wagner, Shannon Sampert, Daisy Raphael, Bailey Gerrits

Bibliographic record

VenueThe International Journal of Press/Politics · 2013
Typearticle
Languageen
FieldSocial Sciences
TopicGender Politics and Representation
Canadian institutionsUniversity of WinnipegUniversity of Alberta
Fundersnot available
KeywordsNewspaperCONTESTPersonalizationPoliticsContext (archaeology)Political scienceMediationPublic relationsGlobeAdvertisingPsychologyBusinessMarketingLawHistory

Abstract

fetched live from OpenAlex

Our study examines the phenomenon of personalization in news coverage of candidates for the leadership of Canadian national political parties. Because the politicization of the personal through newspaper coverage of bodies and intimate lives has different meanings for women and men politicians, we argue that it is important to account for gender differences in levels of personalization. Our analysis of the Globe and Mail newspaper reporting of thirteen party leadership races held between 1975 and 2012 includes eleven competitive women candidates, four of whom won the leadership contest. Conducting a content analysis of 2,463 newspaper articles published over the course of this thirty-seven-year period facilitates comparison of the levels of personalized coverage over time, by leadership contest, and by candidate gender and success. Findings reveal that the amount of personal coverage did not increase over time, as the personalization literature hypothesizes. However, reporting was significantly more likely to “make it personal” for women candidates, as suggested by the literature on media coverage of women politicians. We argue that gendered mediation is largely driving the personalization of political reporting in the Canadian national context

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.435
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.175
GPT teacher head0.351
Teacher spread0.176 · 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 teacher head, not a consensus.

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

Citations62
Published2013
Admission routes2
Has abstractyes

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