Bibliographic record
Abstract
Purpose This short paper builds on and critiques work presenting potential non-disasters: disasters that did not seem to happen despite a major hazard. Previous work does not differentiate among different types of potential non-disasters. This short paper uses local information to propose three categories according to reasons for vulnerability being low or absent. These proposed categories are used to critique the construct of “potential non-disasters”. Design/methodology/approach This short paper uses a subjective approach to examples of potential non-disasters in 2022, focusing on local information that describes what happened. This information is applied and analysed for the three proposed categories using examples from Japan, Nepal, the Philippines and Vietnam. Such comparisons are useful for critiquing “potential non-disasters”, by understanding better local approaches and information available for reporting on situations that could be disasters. Findings Potential non-disasters remain relevant for exploring mechanisms, tools and actions for educating about vulnerability causes and vulnerability reduction to avert disasters. Limitations are evident by relying on media reports, even local ones with local authors. A suggestion is to implement a grant programme for collecting data immediately after a major hazard without an evident, major disaster. Additionally, an annual report and critique of each year's potential non-disasters, categorised and analysed, would help to evidence the presence and limits of the “potential non-disaster” construct. Originality/value This short paper contributes a much deeper theoretical dive into understanding potential non-disasters, both describing them and the drawbacks of the construct. To practitioners, the construct now offers more avenues for actions while illustrating their effectiveness in reducing vulnerabilities. Thus, this paper supports multiple, linked pathways towards more non-disasters.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".