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Record W2969531406 · doi:10.1007/s11266-019-00145-0

Government and Non-profit Collaboration in Times of Deliverology, Policy Innovation Laboratories and Hubs, and New Public Governance

2019· article· en· W2969531406 on OpenAlexaffabout
Kathy L. Brock

Bibliographic record

VenueVOLUNTAS International Journal of Voluntary and Nonprofit Organizations · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicPublic Policy and Administration Research
Canadian institutionsQueen's University
Fundersnot available
KeywordsCorporate governancePublic policyProfit (economics)BusinessNon profitGovernment (linguistics)Public administrationInnovation processEconomicsPublic relationsIndustrial organizationMarketingPolitical scienceFinanceEconomic growthMicroeconomicsWork in process

Abstract

fetched live from OpenAlex

Abstract This article investigates the implications of the move from public administration to new public management to new public governance for relations between the state and non-profit organizations using the example of the development of policy hubs and innovation laboratories under the operational theory of deliverology. Much of the literature suggests that the move towards these collaborative arrangements is providing non-profits with more access and influence in the policy process. Another stream suggests that the changes may be less significant and less positive than assumed for non-profits. This article weighs in with a preliminary examination of policy hubs and innovation laboratories in Canada. It confirms that while collaborative arrangements between the two sectors are expanding and increasingly drawing non-profit actors into the centre of policy-making, non-profit organizations may be wise to heed certain cautions when choosing their partners and terms of the partnerships or they may find their ability to create and influence policy in a meaningful way is limited.

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)0.001
metaresearch head score (Gemma)0.001
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 score0.288
Threshold uncertainty score0.409

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.013
GPT teacher head0.329
Teacher spread0.316 · 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

Citations31
Published2019
Admission routes2
Has abstractyes

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