Collaborative Crisis Management and Nonprofit Governance
Bibliographic record
Abstract
This research delves into the crisis management strategies employed by nonprofit organizations (NPOs) within two diverse neighborhoods of Montreal, Canada, in response to the COVID-19 pandemic. Benefiting from the literature on crisis management and nonprofit governance, the study investigates how NPOs navigated the challenges posed by the pandemic, particularly in addressing the health and social impacts on vulnerable communities. Utilizing a Grounded Theory Methodology, the research unfolds the experiences of various community organizations catering to diverse ethnic and cultural groups. The findings illuminate the governance mechanisms and behaviors, including the use of neighborhood round tables, digital communication strategies, and pragmatic leadership, employed by NPOs to effectively manage the crisis and establish trustworthy networks of collaboration with diverse stakeholders. Furthermore, the study underscores the emergence of bottom-up social processes and a shift in power dynamics, signifying a transition from centralized decision-making by governmental authorities to previously marginalized actors gaining influence on the ground. In conclusion, the research offers practical recommendations for NPO managers, policymakers, and funders, aiming to enhance NPO resilience and performance amidst crises.
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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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.011 | 0.013 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".