Bibliographic record
Abstract
In everyday speech, the word "emergency" usually signifies a sudden and unexpected condition calling for immediate action. In the last four decades, social scientists starting from popular usages of the term, have increasingly attempted to conceptualize emergency as part of the social situation generated by natural and technological disasters or catastrophes. In fact, to a considerable extent, the theoretical work and empirical research on the social aspects of disasters is the equivalent of the social scientific analysis and study of emergencies. Actually whether the term "disaster", "catastrophe" or emergency'' is primarily used, seems to depend on the particular language involved. For example, Italian social scientists have somewhat preferred to use the term "emergency" whereas Americans have been inclined to employ the word disaster" even though the substantive phenomena being discussed is about the same in both cases. However, since most of the social scientific literature that exists in the area uses "disaster" rather than "emergency" or "catastrophe", we will in this article mostly but not exclusively use the first term. Part of this tendency and also lack of complete consensus can be attributed to the fact that social science studies in the area are but about four decades old, and until recently, were primarily undertaken in the United States and Canada.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.309 | 0.129 |
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".