Political Resilience in Canada's Charitable Sector:<b> Are We Prepared?</b>
Bibliographic record
Abstract
Over the last five years, resilience in the Canadian charitable sector has been repeatedly tested, forcing a sector-wide reassessment of traditional operating models.Most recently, a major political shift in the United States, led and exemplified through the electoral victory of Donald Trump and the rise of the Make America Great Again movement, has inspired a "transvaluation of all values" in American politics. This shift has subsequently destabilized Canada's political and economic landscape, impacting traditional corporate funding, major donors’ abilities to give and government funding priorities.Despite the existential challenge faced by our charitable sector in meeting this historical moment, little scholarship has been devoted to exploring the status of our sector’s political resilience and how charities can prepare for and mitigate the impacts of political change.To begin to address this gap, Global Philanthropic Canada embarks upon a focused dive into the practices and perceptions of charitable leaders from across the country. We explore their attitudes, confidence, questions and concerns pertaining to their organization’s ability to adapt to political change. Through analysis of their successes, best practices, challenges and lessons learned, we seek to answer one of the most pressing questions of the day: “Are we prepared?”
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.041 | 0.014 |
| Scholarly communication | 0.012 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 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".