Surviving a Pandemic: The Adaptability and Sustainability of Nonprofit Organizations through COVID-19
Bibliographic record
Abstract
Like many other organizations in Canada and globally, nonprofit organizations have not been insulated from the negative effects of the COVID-19 pandemic. It has affected Canadian nonprofit organizations in numerous ways. This ranges from the effects of COVID-19 on the health of workers and clients to its effect on revenue. As predominantly essential service providers, nonprofit organizations have to find ways to continue operations during the COVID-19 pandemic to ensure that no one is left to fall through the cracks in an uncertain economy. RÉSUMÉ Comme bien d’autres organismes au Canada et dans le monde, les organismes sans but lucratif (OSBL) n’ont pas été épargnés par la pandémie du COVID-19. En effet, pour les OSBL canadiens, celle-ci a eu des incidences dans divers secteurs, allant de la santé des clients et employés jusqu’au revenu. Les OSBL, comme ils sont à toutes fins pratiques des fournisseurs de services essentiels, doivent trouver le moyen de continuer à fonctionner pendant la pandémie afin de s’assurer que personne ne soit oublié dans un contexte économique incertain.
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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 | 0.004 | 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 | 0.007 | 0.008 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.001 | 0.008 |
| 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".