Canadian Government – Non-Profit Relations and COVID-19: Crisis and the Non-Profit Sector
Bibliographic record
Abstract
This study examines the state of the non-profit sector during the ongoing COVID-19 pandemic through the context of government-non-profit relations. The history of this relationship is essential to the understanding of why the non-profit sector has been so negatively affected by this crisis. This research uses a normative institutionalist approach and a qualitative method of analysis to examine how the COVID-19 pandemic has highlighted key issues with the non-profit sector.\nThe study asserts that for a number of reasons, including the lack of a clear vision by the Canadian government for the non-profit sector, shifts in public administration approaches, and stringent and outdated instruments of funding, the sustainability of the non-profit sector is left vulnerable to crises such as the pandemic or the Great Recession of 2008. Additionally, this paper focuses on policy neglect and the obstacles for policy co-operation and creation between the Canadian government and non-profits.\nUltimately, this paper calls for improved relations between the state and non-profit sector to create policies that sustain the sector in order to reduce the severity of the highlighted issues in future crises.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.022 | 0.013 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".