Theoretical Matters: On the Need for Hazard and Disaster Theory Developed Through Interdisciplinary Research and Collaboration
Bibliographic record
Abstract
Hazard and disaster research requires a willingness to step outside of traditional disciplinary ontological and epistemological assumptions to both accommodate and integrate different perspectives. Moreover, the complex qualities of hazards and disasters necessitate interdisciplinary approaches to inform theory development that encompasses environmental, human, and infrastructure systems at multiple scales and units of analysis. Unfortunately, truly integrative hazard and disaster theory at a scale broad enough to account for the many systems and processes involved is currently limited. In this article, we argue that robust hazard and disaster theory can only arise from interdisciplinary research and collaboration. We examine challenges to the development of interdisciplinary hazard and disaster theory, and discuss the characteristics of theory necessary for the goal-oriented nature of research aimed at reducing disaster impact.
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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 | 0.058 | 0.072 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.011 | 0.119 |
| Scholarly communication | 0.022 | 0.036 |
| Open science | 0.006 | 0.019 |
| Research integrity | 0.013 | 0.016 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".