Bibliographic record
Abstract
Purpose This short paper compiles some potential disasters that might not have happened in 2021 even though a major hazard occurred. No definitive statements are made of what did or did not transpire in each instance. Instead, the material offers a pedagogical and communications approach, especially to encourage deeper investigation and critique into what are and are not labelled as disasters and non-disasters—and the consequences of this labelling. Design/methodology/approach This short paper adopts a subjective approach to describing situations in 2021 in which a hazard was evident, but a disaster might not have resulted. Brief explanations are provided with some evidence and reasoning, to be used in teaching and science communication for deeper examination, verification and critique. Findings Examples exist in which hazards could have become disasters, but disasters might not have manifested, ostensibly due to disaster risk reduction. Reaching firm conclusions about so-called “non-disasters” is less straightforward. Originality/value Many reports rank the seemingly worst disasters while research often compares a disaster investigated with the apparently worst disasters previously experienced. This short paper instead provides possible ways of teaching and communicating potential non-disasters. It offers an approach for applying lessons to encourage action on disaster risk reduction, while recognising challenges with the labels “non-disaster”, “success” and “positive news”.
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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".