Risk Reduction and Emergency Preparedness Activities of Canadian Universities
Bibliographic record
Abstract
Preparing for emergencies and disasters has become a necessary part of daily operations of businesses, municipalities, and institutions. Furthermore, educational institutions such as universities, colleges, and schools are not immune to the impacts of disasters (FEMA 443, 2003; Kuban and MacKenzie-Carey, 2001; Miller, 2002; U.S. Department of Education, 2003). Universities are realizing they are exposed to the impact of disasters and that emergency/disaster response requires careful coordination and communication with other organizations and entities that have the resources and skills necessary to manage and respond to particular emergencies (Auf der Heide, 1989; FEMA 443, 2003; Kuban et al., 2001; Mileti, 1999). In Canada, the primary responsibility for emergency preparedness and response is that of the municipality, or local authority, within which a university is located. What then is the role of a university in preparing for and responding to emergencies or disasters? In the absence of compulsory standards, regulations or legislation, universities, based on the survey of this project, are nevertheless reviewing risks and hazards, implementing long-term strategies, and developing relationships with the local municipality. All of these should consider the unique characteristics of the campus environment, which include an open and accessible environment, a functionally separate hierarchy of administrators and academics, a multi-cultural and multi-disciplinary workforce, and a diverse student body, to name a few. After understanding the unique characteristics of the campus environment universities can address emergency preparedness and disaster management by building on the basics of existing and generally accepted standards, such as the NFPA 1600 (NFPA 2000). Additionally, many universities operate much like a municipality (i.e., infrastructure, constituents, and an incorporated government structure) making existing municipal emergency or disaster-related standards, regulations, and legislation also applicable. Further investigation into the application of these standards, regulations, and legislation to the university environment is required to validate the similarities.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.013 | 0.002 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".