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 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".