MétaCan
Menu
Back to cohort
Record W2580829959 · doi:10.1177/0095327x16682785

Add Female Veterans and Stir? A Feminist Perspective on Gendering Veterans Research

2016· article· en· W2580829959 on OpenAlex
Maya Eichler

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueArmed Forces & Society · 2016
Typearticle
Languageen
FieldPsychology
TopicPosttraumatic Stress Disorder Research
Canadian institutionsMount Saint Vincent University
FundersCanadian Institutes of Health ResearchSocial Sciences and Humanities Research Council of CanadaCanada Research ChairsAustralian GovernmentMount Saint Vincent University
KeywordsConceptualizationScholarshipGender studiesPerspective (graphical)PsychologyField (mathematics)Power (physics)Doing genderSociologyGender inequalityMasculinitySocial psychologyInequalityPolitical science

Abstract

fetched live from OpenAlex

This article examines how scholarship on veterans has begun to incorporate gender as a relevant category of research. Drawing on feminist theory, it identifies different approaches to gender within the field of veterans studies and suggests avenues for advancing this aspect of research. The vast majority of gender research on veterans treats gender as a descriptive category or variable through a focus on female veterans or gender differences. This article argues that research on veterans can be enriched by employing gender as an analytical category. Focusing on gender norms, power and inequality based on gender, and the intersections of gender with other categories of social difference opens up new questions for gender research on veterans. This kind of broader, analytical conceptualization of gender reveals the ways in which gender shapes the transition to civilian life for all veterans and how veterans policies and programs impact gender relations.

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.

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)0.000
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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.112
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.194
GPT teacher head0.469
Teacher spread0.275 · 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