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Record W2015445534 · doi:10.1007/s11266-005-3233-y

Devolution of Services to Children and Families: The Experience of NPOs in Nanaimo, British Columbia, Canada

2005· article· en· W2015445534 on OpenAlexafffundabout
Caroline Burnley, Carol R. Matthews, Stephanie McKenzie

Bibliographic record

VenueVOLUNTAS International Journal of Voluntary and Nonprofit Organizations · 2005
Typearticle
Languageen
FieldSocial Sciences
TopicNonprofit Sector and Volunteering
Canadian institutionsUniversity of ReginaVancouver Island University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsDevolution (biology)AccountabilityGovernment (linguistics)Public administrationBusinessPublic relationsService delivery frameworkHuman servicesService (business)Political scienceEconomic growthEconomicsSociologyMarketing

Abstract

fetched live from OpenAlex

This study focuses on the current experience of Nanaimo’s nonprofit family and child service organizations (N = 29) providing services on behalf of government and their adaptation to this devolution. The effects and consequences of contracting on organizational practices, accountability, and services were explored through interviews and focus groups with executive directors, board members, line staff, government representatives, and the United Way. Results show that a significant proportion of funding comes from provincial government contracts. The funding climate is uncertain, and there is considerable confusion, stress, and time involved with the contracting process. Accountability requirements are demanding and nonprofit organizations (NPOs) express concern about a shift to a business management model. Recommendations include a need for increased collaboration between NPOs, a body that speaks for the voluntary sector, and improved relationships between NPOs and government funders.

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)0.002
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.060
Threshold uncertainty score0.433

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0350.007
Scholarly communication0.0040.001
Open science0.0020.006
Research integrity0.0010.004
Insufficient payload (model declined to judge)0.0040.000

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 head0.004
GPT teacher head0.235
Teacher spread0.231 · 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 designQualitative
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

Citations19
Published2005
Admission routes3
Has abstractyes

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