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Record W2277378602 · doi:10.1017/s1755773915000430

Does government funding depoliticize non-governmental organizations? Examining evidence from Europe

2016· article· en· W2277378602 on OpenAlexaff
Elizabeth A. Bloodgood, Joannie Tremblay‐Boire

Bibliographic record

VenueEuropean Political Science Review · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicNonprofit Sector and Volunteering
Canadian institutionsConcordia University
FundersEuropean Commission
KeywordsTransparency (behavior)PoliticsGovernment (linguistics)Unintended consequencesPublic administrationWork (physics)Public economicsEuropean unionPolitical scienceBusinessPublic relationsEconomicsEconomic policyLaw

Abstract

fetched live from OpenAlex

Prior work suggests that government funding can encourage non-governmental organizations (NGOs) to engage in political advocacy and public policy. We challenge this finding and examine two theoretical explanations for the dampening effect of government funding on NGO lobbying. First, donors are known to discipline NGO activity via an implicit or explicit threat to withdraw funding should the organization become too radical or political. Second, NGOs with more radical political agendas are less willing to seek or accept government funding for fear this will limit or delegitimize their activities. Using data from the European Union’s Transparency Register, we find that the share of government funding in NGO budgets is negatively associated with lobbying expenditure. This effect is statistically significant and substantial, which provides a reason for concern about NGO resource dependence. Even when governments are motivated by honorable intentions, their financial assistance has the (unintended) effect of dampening NGOs’ political activity.

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)0.020
metaresearch head score (Gemma)0.056
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.020
Threshold uncertainty score0.107

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0200.056
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0050.010
Science and technology studies0.0010.003
Scholarly communication0.0030.002
Open science0.0010.003
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0060.000

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.

Opus teacher head0.052
GPT teacher head0.334
Teacher spread0.282 · 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 designObservational
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

Citations90
Published2016
Admission routes1
Has abstractyes

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