The Institutional Role of Charity Regulators in Bringing Charities to Account: An International Comparative Study of Charity Regulators in New Zealand, Australia, Canada and England and Wales
Bibliographic record
Abstract
Abstract Accountability in the charity sector continues to be important, especially since charity numbers continue to grow regardless of the economic cycle. Charity regulators play a critical role in managing the charity sector as its primary task is ensuring compliance with the regulatory framework. However, it is also important to evaluate whether the practices and procedures of charity regulators are effective within the broader context of charity accountability. This paper aims to explore charity accountability from the perspective of charity regulators. In common law countries, the charity regulator has oversight over the charity sector and, therefore, has the opportunity to enhance charity accountability. Through the lens of institutional theory, this paper provides an international comparative study of New Zealand, Australia, Canada and England and Wales, all of which share a common law heritage. This study demonstrates how the state regulators in each country have applied different mechanisms of charity accountability by institutionalising the norms and standards required from charities. By identifying and comparing the countries, this paper reveals whether some institutional practices by some regulators have been more effective than others in bringing charities to account.
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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 | 0.011 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.008 | 0.010 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".