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Record W2229727330 · doi:10.1177/1354066115598635

The perils of realist advocacy and the promise of securitization theory: Revisiting the tragedy of the Iraq War debate

2015· article· en· W2229727330 on OpenAlexaff
Eric Van Rythoven

Bibliographic record

VenueEuropean Journal of International Relations · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicGlobal Security and Public Health
Canadian institutionsCarleton University
Fundersnot available
KeywordsSecuritizationFraming (construction)Opposition (politics)Political economySociologyRealismPoliticsScholarshipInternational relationsInternational relations theoryLawLaw and economicsPolitical scienceEpistemologyEconomics

Abstract

fetched live from OpenAlex

Why does realist political advocacy for a more limited national security agenda fail? For nearly two decades, realists in general and Stephen Walt and John Mearsheimer in particular have publicly lamented an endemic problem of threat inflation in America, culminating in the unnecessary 2003 Iraq War. This article argues that understanding the failure of realist advocacy requires appreciating its roots in the model of the marketplace of ideas, an ironically liberal model of discourse that downplays questions of power. As a corrective, I argue that securitization theory and its framing of security debates as discursive, competitive and ultimately power-laden processes offers substantive insights into understanding realism’s anaemic interventions. Focusing specifically on the advocacy of Stephen Walt and John Mearsheimer in their opposition to the 2003 Iraq War, I examine how powerful processes involving social identity and collective emotion came to be turned against realists by their neoconservative interlocutors. In the final section, I suggest that a common research agenda among realism and securitization scholarship is needed to explore their joint interest in the statecraft of threat construction in order to produce practical, politically relevant knowledge.

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)0.013
metaresearch head score (Gemma)0.008
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 score0.776
Threshold uncertainty score0.923

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0130.008
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 head0.023
GPT teacher head0.296
Teacher spread0.273 · 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

Citations18
Published2015
Admission routes1
Has abstractyes

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