Canadian Leapfrog: From Regulating Charitable Fundraising to Co-Regulating Good Governance
Bibliographic record
Abstract
Abstract The regulation of charitable fundraising is no longer just about the regulation of fundraising but about good governance, and increasingly involves co-regulatory regimes which blend elements of self- and state regulation. Canada’s charitable sector has undertaken a bold experiment in creating a comprehensive certification system for good governance, including fundraising, which reframes the target of regulation from the informed donor to the well-performing charity and has the ambitious goal of building a community of practice for self-improvement. At the same time, the federal government has introduced new guidance on fundraising that not only outlines accepted cost to revenue ratios but also specifies standards of good governance. It is an open question as to whether this new self- and state regulation will remain as dual systems or evolve into a hybrid co-regulatory regime in which government integrates sector certification into its own risk management.
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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 | 0.018 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.016 | 0.013 |
| Scholarly communication | 0.014 | 0.003 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.008 | 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".