Charitable Giving in the Time of COVID-19
Bibliographic record
Abstract
It is hard to overestimate the societal disruption that the COVID-19 pandemic has wrought upon Canada. Perhaps no sector has been as adversely impacted as the charitable sector. Yet, do ordinary Canadians see it that way? And if they do, what are they doing about it? To answer these questions the Community Ideas Factory: Creative Behavioural Insights team at Sheridan College in partnership with the Oakville Community Foundation and BEworks conducted scientifically grounded research across Canada (N = 3000) to gain insight into perceptions and behaviours related to charitable giving during the pandemic. The research also explored and compared the factors that incentivize Canadians to donate to charitable causes. This report contains an overview of how the research was conducted, a breakdown of the key results, and discusses the primary takeaways meant to have an immediate impact on charitable fundraising efforts.
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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 | 0.008 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.027 | 0.009 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 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".