From crisis to adaptation: Assessing disaster preparedness and response strategies among Toronto nonprofit organizations during COVID-19
Bibliographic record
Abstract
Framed by social capital theory and the social constructionist theory of disasters, this master’s research project examines the factors influencing the ability of social service nonprofit organizations in Toronto to endure and effectively address disasters. The study explores the crucial role played by these organizations in disaster preparedness and response efforts, highlighting their community engagement expertise, grassroots networks, local knowledge, and adaptability. By utilizing a mixed-methods approach that includes an online survey and key informant interviews, the study gathers insights and recommendations on the disaster preparedness and response strategies of social service nonprofits in Toronto. The research aims to identify the factors that contribute to the resilience of these organizations in the face of disasters and gain an understanding of the current state of disaster preparation and planning in community-based organizations in Toronto. It seeks to provide practical recommendations for improving disaster preparedness and response strategies in the nonprofit sector, emphasizing the significance of social actions, effective communication, collaboration, and resource allocation. This study recognizes disasters as social phenomena shaped by social structures, processes, and practices, emphasizing the intersection between hazards, vulnerable populations, and social context. It underscores the importance of strengthening social networks, addressing structural social inequalities, and promoting collective action to enhance community resilience. The findings of this research study shed light on the valuable role played by nonprofit organizations in disaster preparedness and response efforts, stressing the need for policy changes, additional funding, and collective efforts to strengthen community resilience in the face of disasters. With a comprehensive whole-of-society approach, leveraging the expertise and networks of nonprofit organizations, this study advocates for an enhanced understanding of the factors that contribute to the ability of social service nonprofits to endure and effectively address disasters in Toronto.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".