Nonprofits and community resilience during a pandemic: a France-Quebec perspective
Bibliographic record
Abstract
Purpose This article examines the links between nonprofits and communities’ resilience during the COVID-19 crisis. Previous research on resilience has overlooked nonprofits, with limited studies on their ongoing resilience processes. While nonprofits’ potential to lead their communities’ resilience has been highlighted, we know little about how this potential can be fully achieved. Design/methodology/approach Nonprofit’s potential to lead their communities’ resilience has been highlighted. Yet, nonprofits are also deeply affected by crises, and little is known about their organizational resilience. This study explores the interplay between nonprofits’ organizational resilience and community resilience in the face of crises. We draw from an international comparative case study based on two participatory research designs in France and Quebec during the Covid-19 crisis. Findings The results highlight similarities and differences in how nonprofits’ developed organizational resilience capabilities. These different organizational resilience processes affected in return the reactive and proactive roles the nonprofits could play in community resilience. Research limitations/implications Limitations of the research method include its time boundaries, the specificity of the Covid-19 crisis, which differs from natural hazards which are traditionally studied in the resilience literature (e.g.: Roberts et al., 2021). The unicity of the cases fits the comprehensive purpose of the study, and generalizations of the results are limited. Practical implications Empirically, we offer an original approach of nonprofits and community resilience as ongoing interdependent processes. Originality/value The article contributes to the organizational resilience literature in refining how nonprofits’ characteristics and embeddedness in their community affect their development of resilience capabilities. We theorize the dynamic reciprocal links between nonprofits and community resilience.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".