Bibliographic record
Abstract
Purpose Environmental disasters are preventable, but this remains a complicated and elusive prospect. This article discusses factors that combine to limit and undermine environmental disaster prevention efforts and explores directions for improved theory and practice. Design/methodology/approach The challenge of integrating root cause analysis of environmental disasters with interventions and preventive measures at later stages of disaster incubation is outlined. The prospect of learning and transferring lessons from past environmental disasters is discussed. Eighteen environmental disaster cases are summarized and analyzed. Findings A range of factors, including complexity, lack of lesson transfer, perceived lack of incentives and inaction, limits advances in environmental disaster prevention. Theoretical challenges involve better bridging of root cause and incubation analyses, enhanced understanding of the nature and discipline of foresight and greater documentation of alternative approaches to prevention, including post–normal techniques. Although a transformative breakthrough in environmental disaster prevention is unlikely, substantial progress could be made through better lesson transfer and application of alternative approaches. Originality/value This article draws attention to problems and opportunities surrounding the challenge of environmental disaster prevention and proposes directions for improved theory and practice.
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 | 0.016 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.032 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".