The Perks and Perils of Non-Statutory Fundraising Regulatory Regimes: An Anglo-Irish Perspective
Bibliographic record
Abstract
Abstract This article reviews recent non-statutory attempts at charity fundraising regulation in the UK and Ireland. It explores the definition of success for each regime and examines whether broader policy lessons for fundraising regulation may be learnt from these implementation experiences to date. To this end, the article compares the UK framework for fundraising regulation (via the Fundraising Standards Board) with recent Irish proposals for non-statutory regulation. It also identifies the key challenges facing each regime. The article draws upon current Canadian, Dutch and American regulatory experiences and benchmarks these alternative non-statutory efforts against the Anglo-Irish models by way of comparative analysis. The article tackles the broader question of how we measure success in regulatory terms and argues for better identification of the constituency to be regulated, thereby enabling prioritisation of the salient performance indicators that should be included in any non-statutory framework.
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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 | 0.041 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.007 | 0.028 |
| Scholarly communication | 0.025 | 0.006 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.003 | 0.007 |
| 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".