Institutional perception and support in emergency management in Ontario, Canada
Bibliographic record
Abstract
Purpose The present study seeks to explore the minds and thoughts of emergency management professionals in Ontario in order to better understand the institution, and engage them in a renewed dialogue with communities, academia and other stakeholders. The intention is to strengthen the institution of emergency management, which is the foundation of disaster mitigation. Design/methodology/approach The study is based on interviews conducted with emergency management professionals from the public, private and non‐governmental organization (NGO) sectors in the Province of Ontario, Canada. The questions were a combination of structured and open‐ended questions to elicit rich details on emergency management professionals’ views. Analysis of interview transcriptions highlighted the attitudes and perceptions of the interviewees with respect to themselves, their own organizations, their role in emergency management, and their jurisdictions. The study also provided an opportunity for respondents to provide examples or comments to illustrate their responses. A total of 43 interviews have been analyzed for this paper. Findings The research objective of enhancing the understanding of the institution of emergency management through the minds of emergency management professionals in Ontario has been successfully achieved. It is clear that emergency managers and other professionals engaged in consulting, response and humanitarian assistance activities realize their role and responsibilities quite well. The majority of participants felt that politics and a lack of understanding of one anothers’ roles often limit progress. A clear consensus regarding engaging community players in the development of mitigation strategies and resource allocation emerged in this study. Originality/value The research conducted is the first of its kind in the province of Ontario in Canada. With the help of personal interviews and survey questionnaire, a better understanding of the emergency management institution and professionals working in this field could be realized.
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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.014 | 0.005 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".