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Record W2080197779 · doi:10.1016/j.polsoc.2010.06.001

From shopping to social innovation: Getting public financing right in Canada

2010· article· en· W2080197779 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenuePolicy and Society · 2010
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicCommunity Development and Social Impact
Canadian institutionsQueen's UniversityCarleton University
Fundersnot available
KeywordsContext (archaeology)CitizenshipPoliticsFinancePublic fundingState (computer science)BusinessWelfare stateEconomicsPublic sectorPublic administrationPublic economicsPolitical scienceEconomy

Abstract

fetched live from OpenAlex

Abstract Governments are an important source of funding for the nonprofit and voluntary sector. Yet, the use of funding instruments is conditioned by the political and institutional context. This paper proposes three financing models – charity, welfare state and citizenship – which capture the link between the choice of public financing and the broader institutional context. The financing models are then used to examine the evolution of funding patterns in Canada. We argue that the evolution of financing models in Canada has gradually constrained instrument choice and more importantly, a market-oriented application of funding instruments has dominated the financing debates at the expense of a broader focus on preconditions of applying the instruments effectively. As a result, funding instruments in Canada are poorly suited for fostering innovation and investing in capacity development in the voluntary sector.

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.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.489
Threshold uncertainty score0.454

Codex and Gemma teacher scores by category

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

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 head0.045
GPT teacher head0.256
Teacher spread0.211 · 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