Bibliographic record
Abstract
Abstract All Is Well attempts to answer one of the most urgent questions of our time: What is the relationship between modern states and the disasters they claim to manage? Disasters are commonly understood as exceptional occurrences that ruin societies and inspire ad hoc rituals of legal, administrative, and scientific control called “disaster management.” States and the international institutions perform disaster management to protect society. The book challenges this traditional narrative. It interprets “disaster management” as a historical struggle to conservate the existence and experience of catastrophes and produce idealized authorities capable of protecting society from uncertainty. It examines the emergence of this struggle in the eighteenth century and reveals how rulers and experts struggling to master God, nature, and each other inaugurated modern meanings of risk, normalcy, power, and responsibility. By recovering this history of disaster management, the book reveals underlying knowledge structures and political economies that smuggle the unspoken costs of modernity inside the rationalized representation of past catastrophes and future risks. Catastrophes, put bluntly, are not occurrences. They are inventions. Even in their most destructive forms, catastrophes are the stigmata through which the modern state renews itself. The book develops this argument by examining the Marseille plague (1720), the Lisbon earthquake (1755), and the Bengal famine (1770) and showing how eighteenth-century beliefs reverberate in structure and policies of “global” disaster management today. It concludes that climate change and the national and international authorities designed to fight it are products of three centuries of disaster management, and civilizational survival depends on reckoning with this past.
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.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.199 | 0.109 |
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".