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Record W2003970540 · doi:10.1177/1750635210360082

‘The vermin have struck again’: dehumanizing the enemy in post 9/11 media representations

2010· article· en· W2003970540 on OpenAlexaff
Erin Steuter, Deborah Wills

Bibliographic record

VenueMedia War & Conflict · 2010
Typearticle
Languageen
FieldArts and Humanities
TopicRhetoric and Communication Studies
Canadian institutionsMount Allison University
Fundersnot available
KeywordsDehumanizationFraming (construction)NewspaperMedia studiesRhetoricRhetorical questionNarrativeAestheticsSociologyHistoryPolitical scienceLiteratureLawArtPhilosophy

Abstract

fetched live from OpenAlex

Much of the scholarly attention surrounding the media’s treatment of the so-called ‘war on terror’ has focused on its uncritical replication of the Bush administration’s rhetorical framing of the conflict, in which the September 11 attacks were seen as acts of war initiating a retaliatory war on terror. While this dominant, martial framing is now being challenged, an equally significant framing remains largely unexamined, one as significant to media rhetoric and public perception as the war trope itself. This article identifies the consistent pattern of dehumanizing metaphor that dominates Western media’s coverage. It focuses on newspaper headlines as influentially compressed narratives replicating and recycling key metaphors that systematically figure the enemy as animal, vermin, or metastatic disease. These dehumanizing media representations, which have historically prefigured abuse, oppression, and even genocide, are being circulated as uncritically through newspaper media headlines as Bush’s war framing was initially and, we argue, now requires the same critical dismantling.

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)0.005
metaresearch head score (Gemma)0.009
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 score0.011
Threshold uncertainty score0.025

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.009
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0100.033
Scholarly communication0.0110.008
Open science0.0010.006
Research integrity0.0030.004
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 head0.060
GPT teacher head0.282
Teacher spread0.222 · 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

Citations111
Published2010
Admission routes1
Has abstractyes

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