National Nonprofit Sector Advocacy During the COVID-19 Pandemic
Bibliographic record
Abstract
When the COVID-19 pandemic hit Canada in March 2020, charitable and nonprofit sector leaders quickly realized the survival of many organizations was at risk. Three national coalitions formed to seek support for the sector from the federal government. Their efforts produced several concrete policy outcomes, including the inclusion of charities and nonprofits in all major federal relief programs and two support programs designed for charities and nonprofits. They also contributed to significantly increased awareness among policymakers of the role and challenges of charities and nonprofits. This has opened a policy window that the sector can use to advance several long-standing goals. RÉSUMÉ Quand la pandémie du COVID-19 a frappé le Canada en mars 2020, les dirigeants du secteur caritatif et sans but lucratif se sont vite rendu compte que la survie de plusieurs organismes était menacée. On a donc formé trois coalitions nationales afin de chercher un appui au secteur auprès du gouvernement fédéral. Les efforts de ces coalitions ont mené à plusieurs politiques concrètes, y compris l’inclusion d’organismes de bienfaisance et sans but lucratif dans tous les programmes d’aide fédéraux majeurs et la création de deux programmes d’aide conçus spécifiquement pour les organismes de bien- faisance et sans but lucratif. Ces coalitions ont aussi contribué à accroître de manière significative la conscience parmi les stratèges du rôle et des défis des organismes de bienfaisance et sans but lucratif. Ces progrès ont créé des occasions politiques dont le secteur pourra profiter pour faire avancer plusieurs objectifs de longue date.
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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 | 0.008 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.005 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.011 | 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".