Teaching disaster risk management: lessons from the Rotman School of Management
Bibliographic record
Abstract
Este artículo describe cómo se enseñan los temas de la gestión del riesgo de desastres en la escuela de administración, Rotman School of Management, de University of Toronto y, de esta manera, resalta las oportunidades para desarrollar módulos de cursos de gestión de riesgo de desastres similares. Un curso de pregrado y uno electivo de MBA, llamado Falla Catastrófica en las Organizaciones, contiene cuatro módulos que son directamente relevantes para la gestión del riesgo de desastres. El primer módulo se enfoca en la necesidad de pasar de la indiferencia a la sensibilidad al riesgo. El segundo módulo toma en cuenta la importancia de la continuidad de negocio y los planes de gestión de crisis y explora las deficiencias que tienen en común. El tercer módulo utiliza un estudio de caso para examinar el tema de la gestión prospectiva del riesgo. El cuarto módulo se enfoca en la vulnerabilidad de las cadenas de suministro y otros sistemas complejos del riesgo de desastres. El artículo describe los detalles de la implementación de estos módulos y discute las oportunidades para una integración más profunda de los temas de gestión de riesgo de desastres en otras partes del currículo.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".