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.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.028 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".