The logistics of fear: violence and the stratifying power of emotion
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
This article contributes to growing sociological interest in theorising fear by providing cross-class evidence of what people do when they are afraid and how their emotion strategies matter for broader inequalities. Drawing on and extending pragmatist approaches to the study of emotion, I conceptualise the logistics of fear as the strategies that people employ to manage fear when prompted by a large-scale threat at the societal level. I argue that fear in such contexts can quickly exacerbate inequality by means of the unequal resources people draw on to solve or manage fear on a daily basis. Drawing on qualitative fieldwork conducted in the midst of a violent criminal war in urban Mexico, I trace the restructuring of metropolitan nightlife as a three-stage process: destruction, dispersion, and classed re-concentration. Attention to classed variations in emotion strategies over time provides evidence of the destructive and creative facets of fear, as well as of its stratifying power. More broadly, this research puts forth a pragmatist approach to the study of emotion that centres emotion as a problem and social process.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| 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