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Record W2001650038 · doi:10.1386/jwcs.2.2.195_1

The soldier as hunter: pursuit, prey and display in the War on Terror

2009· article· en· W2001650038 on OpenAlexaff
Deborah Wills, Erin Steuter

Bibliographic record

VenueJournal of War and Culture Studies · 2009
Typearticle
Languageen
FieldSocial Sciences
TopicMilitary History and Strategy
Canadian institutionsMount Allison University
Fundersnot available
KeywordsTrope (literature)AdversaryIntersection (aeronautics)Movie theaterPoliticsTrophyConstruct (python library)Government (linguistics)Media studiesAestheticsSociologyHistoryPolitical scienceLiteratureLawArtGeographyLinguisticsComputer securityCartographyPhilosophy

Abstract

fetched live from OpenAlex

In both political speech and news media discourse, the War on Terror has been persistently metaphorically figured as a hunt for prey. In concert with this figuration, a trope of the soldier as hunter has risen to cultural prominence. This paper examines official government discourses' use of the soldier-as-hunter paradigm, military language and training practice that reflects and augments this paradigm, and media language that uncritically echoes and furthers it. The discursive intersection of government, military, and media language is suggestively reflected in the wars in Afghanistan and Iraq through soldier's self-representations, particularly through trophy-taking and display. These behaviours evidence the potency, coherence, and influence of the soldier-hunter trope in the ways soldiers construct and circulate images of both themselves as hunter and the enemy as prey.

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)0.005
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 score0.014
Threshold uncertainty score0.028

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0100.021
Scholarly communication0.0080.005
Open science0.0010.006
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.022
GPT teacher head0.332
Teacher spread0.310 · 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

Citations19
Published2009
Admission routes1
Has abstractyes

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