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Record W2916518432 · doi:10.3224/eris.v5i3.07

On Backlash: Emotion and the Politicisation of Security

2018· article· en· W2916518432 on OpenAlex
Eric Van Rythoven

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueEuropean Review of International Studies · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicGlobal Security and Public Health
Canadian institutionsCarleton University
Fundersnot available
KeywordsBacklashReactionaryHostilityCertaintyKey (lock)EpistemologySociologyPolitical scienceLaw and economicsSocial psychologyPsychologyLawComputer securityComputer sciencePhilosophyPoliticsArtificial intelligence

Abstract

fetched live from OpenAlex

This article explores the role of emotion in the politicisation of security through the concept of backlash: the idea of visceral and reactionary episodes where security claims are adamantly rejected and the subject of ‘security’ becomes intensely controversial. Starting by examining the role of emotion in politicisation, I make the case for viewing emotions as playing a key role in the distribution of certainty in security discourse. Building on this epistemic view of emotion, I review how backlash is understood in other fields before tailoring a definition for security studies centered around four constitutive features: reaction, hostility, emotion, and contagion. The final section focuses on the politicising effects of backlash including the mobilisation of backlash movements, the intensification of controversy, and arena shifting. The discussion concludes by suggesting that the concept of backlash offers a promising research agenda for those inquiring into the politicisation of security.

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.

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)0.003
metaresearch head score (Gemma)0.003
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: none
Teacher disagreement score0.945
Threshold uncertainty score0.346

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.043
GPT teacher head0.389
Teacher spread0.346 · 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