Crisis beyond the exceptional: The latent, everyday nature of the crisis perpetual
Bibliographic record
Abstract
We are surrounded by declarations of crises, from climate to housing, debt and beyond. Crisis is everywhere and yet it remains exceptional. A crisis is imagined as a call to action, a repudiation of the old system, promising change if only the moment can be seized. And yet, as Roitman (2014) argues, crisis declarations generally reinforce the status quo. Building on this insight, I argue that ‘fixing’ a crisis—configured as exceptional—involves pushing the crisis down to and atomizing it across vulnerable populations, further increasing precarity. This paper delves into crisis from this perspective. Not from the macro scale of the state, the economy, or the global, but from the everyday crises that are an ever‐lurking and constant reality for so many living in conditions of poverty, violence and exclusion. Asking how these myriad, hidden crises relate to the crisis exceptional, I argue that their constant reproduction is essential to staving off broader economic and social crises and in reasserting the status quo when such broader crises can no longer be contained. In this way, crisis originates not from the macro scale, at which we usually think of crisis, but from the proliferation of everyday crises across vulnerable populations.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.027 |
| Scholarly communication | 0.011 | 0.018 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".