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Record W1960458331 · doi:10.4324/9781315600659-17

Border Wars: Narratives and Images of the US-Mexican Border on TV

2014· article· en· W1960458331 on OpenAlexvenueno aff
Reece Jones

Bibliographic record

VenueACME: An International Journal for Critical Geographies · 2014
Typearticle
Languageen
FieldSocial Sciences
TopicMedia Studies and Communication
Canadian institutionsnot available
Fundersnot available
KeywordsNarrativeGeopoliticsMilitarismDramaRepresentation (politics)StorytellingTerrorismHistoryMedia studiesPolitical scienceSociologyAestheticsVisual artsLawPoliticsLiteratureArt

Abstract

fetched live from OpenAlex

This paper analyzes the visual and narrative representation of the US-Mexico border in the National Geographic television show Border Wars. The show is significant because it brings the hidden and often opaque borderlands into the homes of millions of Americans, and viewers around the world, every week. It transforms the unknown space of the border into a series of images and stories that create a coherent narrative for the viewer. The representation of the border emphasizes threat and danger through the constant repetition of particular phrases (terrorism, war, cartel foot soldiers) and images (guns, high-speed chases, Black Hawk helicopters, Predator drones). Despite the militaristic lead-ins to each episode, the dramatic music, and the heightened drama of the storytelling, in the end most of the episodes present a more prosaic border landscape of poor migrant workers looking for a better life. This disjuncture between the official narrative of the border and the images of what happens in the show provide a crucial insight into the role popular geopolitical narratives play in creating a version of reality and convincing the public that the ‘problem’ of the border needs a securitized and militarized response.

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)0.001
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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.622
Threshold uncertainty score0.655

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
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 science0.0010.000
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 head0.020
GPT teacher head0.398
Teacher spread0.377 · 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 designTheoretical or conceptual
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

Citations24
Published2014
Admission routes1
Has abstractyes

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