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Record W2951945404 · doi:10.1515/npf-2019-3001

Retraction of: New Public Governance and the Growth of Co-Located Nonprofit Centers

2018· article· en· W2951945404 on OpenAlexaboutno aff
Diane Vinokur‐Kaplan

Bibliographic record

VenueNonprofit Policy Forum · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicDisaster Management and Resilience
Canadian institutionsnot available
Fundersnot available
KeywordsCorporate governanceBusinessNonprofit organizationSpace (punctuation)Order (exchange)Public relationsPublic administrationService (business)Social needsPolitical scienceMarketingFinanceHealth care

Abstract

fetched live from OpenAlex

New Public Governance urges public services to collaborate with other relevant organizations in order to increase the efficiency and effectiveness of provided services. A relevant venue for such collaborations are co-located nonprofit centers, facilities that offer affordable, shared space workplaces for nonprofits and other social-benefit organizations. Many of these centers actively encourage collaboration among their tenants, especially in facilities organized to house related service-providers. They also provide comfortable space for meetings with public sector agencies and other funders. Such centers have been growing in the twenty-first century; nearly 400 have been identified in the United States and Canada (Nonprofit Centers Network 2015a), and they now house hundreds of various nonprofits organizations. This article describes these centers’ goals, history, and trends that encouraged their development, and aspects of their architecture and design. Examples of co-located nonprofit centers that provide an array of social services are presented, from the U.S. and Canada. In sum, these centers help advance the quality of life for clienteles and communities; and the collaborations and networks that they establish promote a key goal of New Public Governance.

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

Direct model labels (unvalidated)

Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.

Model armCategoriesStudy designConfidence
gemmaResearch integrity
Domain: not available · Genre: Editorial
About the Canadian research system: no · About a Canadian topic: no
Not applicablehigh
gptResearch integrity
Domain: not available · Genre: Editorial
About the Canadian research system: no · About a Canadian topic: no
Not applicablehigh
models agreeAgreement compares identical category sets and study designs across arms.

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)0.006
metaresearch head score (Gemma)0.077
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesResearch integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.986
Threshold uncertainty score0.066

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.077
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0060.005
Scholarly communication0.0070.006
Open science0.0030.004
Research integrity0.0140.023
Insufficient payload (model declined to judge)0.0200.009

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 head0.023
GPT teacher head0.315
Teacher spread0.292 · 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

Labeled directly by 2 models reading the full record.

Study designNot applicable
Domainnot available
GenreEditorial

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

Citations2
Published2018
Admission routes1
Has abstractyes

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