<b>Defending Philanthropy: Understanding the impact, challenges and necessary evolution of the Canadian philanthropic sector</b>
Bibliographic record
Abstract
The work of non profit organizations is transformational – from tangible grassroots impact in our communities, to national organizations driving for change at the highest levels.But the philanthropic sector does not exist without critique. “The pandemic has emphasized the importance of the non profit sector's work, but it has also laid bare the cracks in our operating environment", said the 2022 Imagine Canada paper “Diversity Is Our Strength”.As all industries grapple with their future in a post-pandemic landscape, so too does the philanthropic, considering broad-based topics like: funding challenges, the work required of us all to address equity, diversity and inclusion and how we create safe spaces for our teams and stakeholders, attracting and retaining the best talent, transparency within our operations, and adjusting course to be relevant and impactful in the future.Using a variety of research methods, Global Philanthropic Canada set out to engage practitioners, leaders, and industry commentators within the philanthropic sector in Canada to learn more.To learn more about this paper and Global Philanthropic Canada's work, visit us at https://globalphilanthropic.ca .
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.019 | 0.012 |
| Scholarly communication | 0.018 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.043 | 0.005 |
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".