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Record W4416655434 · doi:10.29173/cjnser801

Biting the Hand that Feeds You? Exploring Whether (and How) Government Funding Constrains Charities’ Engagement in Public Policy

2025· article· en· W4416655434 on OpenAlexafffundvenueabout
John Cameron, Heather Dicks, Liam Swiss

Bibliographic record

VenueCanadian journal of nonprofit and social economy research · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicNonprofit Sector and Volunteering
Canadian institutionsMemorial University of NewfoundlandAcadia UniversityDalhousie University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsInsiderAgency (philosophy)Government (linguistics)PoliticsPublic policyRevenueCampaign finance

Abstract

fetched live from OpenAlex

This article examines relationships between federal government funding and reported spending by charities on what the Canada Revenue Agency defined as “political activities” over the period 2003–2017. Anecdotal evidence suggests that many charities in Canada see dependence on government funding by other charities as a factor that limits policy engagement. Comparative research generally suggests that government funding is positively associated with policy engagement by charities, but also that it channels charities away from outsider or contentious forms of advocacy toward less confrontational, insider strategies. The CRA data on political activities analyzed here adds important insights because it tracks outsider advocacy involving public calls to action, which government funding is expected to constrain. This analysis finds that charities with federal funding were more likely to report political activities than those with no federal funding, but only to a point and with important differences based on the size of charities.

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)0.008
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: Empirical
Teacher disagreement score0.481
Threshold uncertainty score0.968

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.036
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0060.004
Scholarly communication0.0060.003
Open science0.0010.003
Research integrity0.0010.002
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 head0.292
GPT teacher head0.380
Teacher spread0.087 · 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

Citations0
Published2025
Admission routes4
Has abstractyes

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