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Record W1966277036 · doi:10.1177/0020702014543708

Engendering two solitudes? Media representations of women in combat in Quebec and the rest of Canada

2014· article· en· W1966277036 on OpenAlexaffabout
Krystel Chapman, Maya Eichler

Bibliographic record

VenueInternational Journal Canada s Journal of Global Policy Analysis · 2014
Typearticle
Languageen
FieldSocial Sciences
TopicGender, Security, and Conflict
Canadian institutionsMount Saint Vincent UniversityRoyal Military College of Canada
Fundersnot available
KeywordsNewspaperNarrativeRest (music)Media coverageSociologyGender studiesIdentity (music)Construct (python library)Media studiesForeign policyPolitical scienceHistoryLawMedicineAestheticsLiteraturePolitics

Abstract

fetched live from OpenAlex

This article brings gender into the two-solitudes debate in Canadian foreign and defence policy by analyzing English- and French-Canadian newspaper coverage of women in combat in Afghanistan. We argue that there are no “two solitudes”—no national divisions are apparent between Quebec and the rest of Canada (ROC) when it comes to media representations of women in combat. Our findings confirm what other scholars have recently argued, which is that differences between the two solitudes on issues of defence policy may be less significant than often stated. The narrative of female combat soldiers presented in the media helps construct a pan-Canadian identity around the idea of Canada’s progressiveness on military gender integration. We also found that the extent to which the death of a female combat soldier received media attention was largely based on her origin from Quebec or the ROC. These differences lead us to conclude that a selective heroization of soldiers on the basis of their origins affects Canadian media coverage of the war.

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)0.003
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.161
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.012
GPT teacher head0.319
Teacher spread0.307 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations12
Published2014
Admission routes2
Has abstractyes

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