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Record W2964188068 · doi:10.1111/cars.12251

How the State Shaped the Nonprofit Sector: Public Funding in British Columbia

2019· article· en· W2964188068 on OpenAlexaffabout
Dominique Clement

Bibliographic record

VenueCanadian Review of Sociology/Revue canadienne de sociologie · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicNonprofit Sector and Volunteering
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsThrivingNonprofit sectorPublic administrationState (computer science)PoliticsGovernment (linguistics)Public sectorPublic fundingPolitical scienceScope (computer science)Private sectorPower (physics)Economic growthBusinessPublic relationsEconomicsSociologyLawSocial science

Abstract

fetched live from OpenAlex

This article examines how the state has used its spending power to shape the nonprofit sector in British Columbia since the 1960s. The province's thriving nonprofit sector and its polarized political culture provide an ideal case study for exploring the relationship between the state and nongovernmental organizations. The following study documents changes in state policy, the trajectory of funding, funding patterns, and organizations that have received state funding. Although public funding for nonprofits in Canada has been pervasive for decades, there is little empirical evidence on the nature and scope of this funding. This article is based on an innovative new database that provides a comprehensive list of grants from the provincial government to nonprofit organizations between 1960 and 2014. Despite concerns regarding cuts to public funding in recent years, this study finds that there has been an overall increase in funding. However, there has also been a significant shift in funding from women's issues to Aboriginal peoples since the early 2000s.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation 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 score0.218
Threshold uncertainty score0.857

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.002
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.067
GPT teacher head0.272
Teacher spread0.205 · 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 teacher head, 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

Citations10
Published2019
Admission routes2
Has abstractyes

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Same venueCanadian Review of Sociology/Revue canadienne de sociologieSame topicNonprofit Sector and VolunteeringFrench-language works237,207