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Record W2080013659 · doi:10.1007/s11266-011-9237-x

Canadian Leapfrog: From Regulating Charitable Fundraising to Co-Regulating Good Governance

2011· article· en· W2080013659 on OpenAlexafffundabout
Susan D. Phillips

Bibliographic record

VenueVOLUNTAS International Journal of Voluntary and Nonprofit Organizations · 2011
Typearticle
Languageen
FieldSocial Sciences
TopicNonprofit Sector and Volunteering
Canadian institutionsCarleton University
FundersMuttart Foundation
KeywordsCertificationCorporate governanceRevenueGovernment (linguistics)Government regulationState (computer science)Good governanceBusinessPublic administrationPublic relationsEconomicsAccountingPolitical scienceFinanceManagementLaw

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.018
metaresearch head score (Gemma)0.036
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.882
Threshold uncertainty score0.859

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0180.036
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0030.002
Science and technology studies0.0160.013
Scholarly communication0.0140.003
Open science0.0040.007
Research integrity0.0070.009
Insufficient payload (model declined to judge)0.0080.001

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.

Opus teacher head0.023
GPT teacher head0.286
Teacher spread0.262 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations50
Published2011
Admission routes3
Has abstractyes

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