Learning to feel, learning to fear? Emotions, imaginaries, and limits in the politics of securitization
Bibliographic record
Abstract
Abstract Despite a growing interest in the role of emotions in world politics, the relationship between emotion and securitization remains unclear. This article shows that persistent, if sporadic, references to fear and emotion in securitization studies remain largely untheorized and fall outside conventional linguistic and sociological ontologies. The tendency to discuss emotion but deny it ontological status has left securitization theory incoherent. This article offers a theoretical reconstruction of securitization where emotion, specifically collective fears, serve as the locus of an audience’s judgment for the practice of securitization. Yet rather than simply accepting that fear facilitates securitizing moves, the article draws on appraisal theory from psychology to argue that collective fear appraisals are often fragile cultural constructs. The generation of these emotional appraisals is often constrained by the limited symbolic resources of the local security imaginary and how agents contest and employ these resources. When the capacity to generate collective fears is constrained, so too is the practice of securitization. An empirical discussion of threat images in US foreign policy is used to explore these constraints. The tendency for securitizing moves to be interpreted as comic underscores the precariousness of social practices seeking to elicit particular collective emotions.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.046 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.000 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".