Mitigating natural disasters: The role of eco-ethics
Bibliographic record
Abstract
David Etkin currently serves as Coordinator of the Program on Emergency Management at York University, Toronto, Canada. He worked for Environment Canada from 1977 to 2005, collaborating on teaching and research projects over the last ten years with members of the Institute for Environmental Studies at the University of Toronto. His area of expertise is natural hazards and disasters. He has 55 publications to his credit, 23 of which are in peer-reviewed journals. He has participated in several international projects dealing with disaster studies, and was Principal Investigator on the Canadian Natural Hazards Project. Ingrid Leman Stefanovic is Director, Centre for Environment, at the University of Toronto and a former member of the Executive Council of the World Society for Ekistics. Her area of teaching and research is environmental philosophy, with a special interest in how values and perceptions affect environmental decision making. Her most recent book is entitled Safeguarding Our Common Future: Rethinking Sustainable Development (SUNY, 2000). Contact Professor Stefanovic at Centre for Environment, University of Toronto, 33 Willcocks Street, Toronto, Ontario.M5S 3E8 or email ingrid.stefanovic@utoronto.ca. The text that followsis a slightly edited version of a paper published in Mitigation and Adaptation Strategies for Global Change, vol. 10, pp. 467-490 (Springer, 2005).
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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 | 0.010 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.057 |
| Scholarly communication | 0.012 | 0.009 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".