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Record W2160216926 · doi:10.1177/0899764003260601

Funding Relations between Nonprofits and Government: A Positive Example

2004· article· en· W2160216926 on OpenAlexaffabout
Laura K. Brown, Elizabeth Troutt

Bibliographic record

VenueNonprofit and Voluntary Sector Quarterly · 2004
Typearticle
Languageen
FieldSocial Sciences
TopicNonprofit Sector and Volunteering
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsGovernment (linguistics)AccountabilityInvestment (military)Transaction costBusinessPublic relationsService (business)Database transactionPublic administrationFinancePoliticsMarketingPolitical science

Abstract

fetched live from OpenAlex

This article examines the attributes of a successful contracting model for the financing and support of nonprofit organizations. It describes how, through government initiative, a program can be built in which transaction costs are minimized through a cooperative approach to contracting based on mutual trust. It shows how investment in a long-term, trust-based, cooperative relationship underlined by professional standards and a continuous focus on a common mission by all levels of actors within and without government can provide the impetus for a system in which high standards of service are maintained, accountability is organic, and organizations feel supported in their mission but not controlled. The example presented is a provincial government program for the prevention of family violence in Manitoba, Canada, but the features that make it successful can be applied widely.

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)0.010
metaresearch head score (Gemma)0.016
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.025
Threshold uncertainty score0.074

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.016
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0150.012
Scholarly communication0.0100.007
Open science0.0010.008
Research integrity0.0050.004
Insufficient payload (model declined to judge)0.0050.001

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 head0.030
GPT teacher head0.271
Teacher spread0.240 · 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
Published2004
Admission routes2
Has abstractyes

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