COVID-19: State of the Ontario Nonprofit Sector One Year Later
Bibliographic record
Abstract
In spring 2021, the Ontario Nonprofit Network (ONN) and l'Assemblée de la Francophonie de l'Ontario (l'AFO) engaged nonprofit Community Researchers to conduct a bilingual survey of Ontario nonprofits. The focus was on the experiences of nonprofits during the pandemic and, in particular, the state of their operations in 2020-21, along with the adequacy of governmental relief measures to support nonprofits during the emergency. This followed a previous survey conducted by ONN and l'AFO in spring 2020.Responses reveal much about the dedicated efforts nonprofits have made to continue serving communities, the fragmented and inadequate government measures to respond to the COVID-19 crisis, and the work ahead as Ontario transitions into a recovery.The survey was open to all nonprofits in Ontario, including charities, nonprofit cooperatives and grassroots groups, with a mission to serve a public benefit. It was conducted between May 17- June 4, 2021 and received 2,983 responses. The survey technical report includes all data cross tabulated by region, sector, size, and language of operation. De-identified data sets are publicly available on the ONN website.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.019 | 0.002 |
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".