MétaCan
Menu
Back to cohort
Record W4200492824 · doi:10.1177/17506352211059130

Iconic war images and the myth of the ‘good American Soldier’

2021· article· en· W4200492824 on OpenAlexaff
Megan MacKenzie

Bibliographic record

VenueMedia War & Conflict · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicGender, Security, and Conflict
Canadian institutionsSimon Fraser University
FundersDanmarks Frie Forskningsfond
KeywordsMythologyMasculinityNarrativeIdeal (ethics)VirilitySociologyGender studiesAestheticsWhite (mutation)Identity (music)RomanceNationalismHistoryLiteratureArtPolitical scienceLawPolitics

Abstract

fetched live from OpenAlex

This article explores the ‘good American soldier’ as a gendered ideal type shaped by, and reproductive of, myths about American military success, romantic notions of small-town working and white America, notions of heterosexual virility, and ableist stereotypes about personal resilience. Drawing from an analysis of 10 years of media coverage of an iconic image dubbed the ‘Marlboro Marine’, the article outlines three specific myths linked to the ‘good American soldier’, in order to provide an insight into ideals of militarized masculinity and the gendered myths that shape American nationalism and identity. In developing this analysis, the article extends existing work on military masculinities by introducing the ‘good American soldier’ ideal type and explores the multiple myths associated with this ideal type. The article also demonstrates how a media narrative analysis that covers an extended period of time makes it possible to observe shifting narratives associated with the ‘good American soldier’.

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)0.003
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: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.001
Science and technology studies0.0080.024
Scholarly communication0.0060.007
Open science0.0010.004
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0040.000

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 head0.019
GPT teacher head0.274
Teacher spread0.255 · 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

Citations9
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueMedia War & ConflictSame topicGender, Security, and ConflictFrench-language works237,207