Learning to feel, learning to fear? Emotions, imaginaries, and limits in the politics of securitization
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.
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.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it