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Record W654677962

Delicate Dances: Public Policy and the Nonprofit Sector

2003· book· en· W654677962 on OpenAlexaboutno aff
Kathy L. Brock

Bibliographic record

VenueMedical Entomology and Zoology · 2003
Typebook
Languageen
FieldSocial Sciences
TopicReligion, Society, and Development
Canadian institutionsnot available
Fundersnot available
KeywordsPublic administrationPublic sectorPolitical scienceCorporate governancePublic policySociologyManagementLawEconomics
DOInot available

Abstract

fetched live from OpenAlex

The authors look at the relationships in different provincial settings, focusing on Ontario, Quebec, and Saskatchewan, examining the defining influence of government welfare programs on the lives of two local religious orders in Atlantic Canada. The authors argue that both the public and the nonprofit sectors are changing. In the public sector, the traditional dominance of central governments has given way to a governance system that interweaves action at the global, national, regional and local levels. In the nonprofit sector, groups are assuming new organizational forms and engaging in public policy more centrally, both as advocates and service providers. Not surprisingly, relations between these two sectors involve a complex series of delicate dances, in which missteps by either partner can produce tangled confusion. It includes contributors such as: Donald Abelson (University of Western Ontario), Kathy Brock (Queens University), Ian Brodie (University of Western Ontario), Ann Capling (Melbourne University), Miriam Lapp (University of Western Ontario), Georges leBel (UQAM), Heidi Macdonald (University of Lethbridge), David Malloy (University of Regina), Kim Nossal (Queens University), Susan Phillips (Carleton University), Ken Rasmussen (University of Regina), Paul Pross (Dalhousie University), Peter Smith (Athabaska University), Elizabeth Smythe (Concordia University College of Alberta), Kernaghan Webb (Carleton University), and Mary Wiktorowicz (York University).

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)0.003
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.406
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.008
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.025
GPT teacher head0.305
Teacher spread0.281 · 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.

Study designTheoretical or conceptual
Domainnot available
GenreOther

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

Citations17
Published2003
Admission routes1
Has abstractyes

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