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Record W2788933898 · doi:10.5539/ijel.v8n3p15

Dr. Strangelove or How I Should Stop Worrying and Love Fascism

2018· article· en· W2788933898 on OpenAlexvenueno aff
André Luís Mattedi Dias

Bibliographic record

VenueInternational Journal of English Linguistics · 2018
Typearticle
Languageen
FieldArts and Humanities
TopicLiterature, Film, and Journalism Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsParanoiaCold warOrder (exchange)Nuclear weaponThe HolocaustGovernment (linguistics)DemocracyPeriod (music)Political scienceSociologyLawHistoryPsychologyAestheticsPhilosophyLinguisticsEconomicsPolitics

Abstract

fetched live from OpenAlex

This paper presents a Foucauldian discourse analysis of Stanley Kubrick’s Dr. Strangelove or: How I Learned to Stop Worrying and Love the Bomb. The analysis examines linguistic and extralinguistic aspects of both the film and the novel. It is composed of three parts: the first is an analysis of the Manichaeism during the Cold War period and how it turned the Soviets into mortal enemies of the United States; the second is how the nuclear threat and the Cold War paranoia could destroy the democratic system in the United States; and the third analysis explain how Fascistic relations could be cultivated through the discipline of bodies. It has been concluded that the movie is presenting a concept, here referred to as Strangelove’s Hypothesis, that a Strangelovian scenario (i.e., a nuclear holocaust, usually caused by incompetence or without the will to do so) could lead to the emergence of a Fascistic-like form of government in order to restore security. The solution presented to avoid such scenario is a sociopsychological change in order to pursue more peaceful 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.

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)0.000
metaresearch head score (Gemma)0.013
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Scholarly communication, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.791
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.013
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.044
GPT teacher head0.287
Teacher spread0.243 · 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.

Study designNot applicable
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

Citations0
Published2018
Admission routes1
Has abstractyes

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Same venueInternational Journal of English LinguisticsSame topicLiterature, Film, and Journalism AnalysisFrench-language works237,207