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Record W2072111276 · doi:10.1080/16184740701270329

An Assessment of Sport Canada's Sport Funding and Accountability Framework, 1995–2004

2007· article· en· W2072111276 on OpenAlexaffabout
Eva P. Havaris, Karen Danylchuk

Bibliographic record

VenueEuropean Sport Management Quarterly · 2007
Typearticle
Languageen
FieldSocial Sciences
TopicNonprofit Sector and Volunteering
Canadian institutionsWestern University
Fundersnot available
KeywordsAccountabilityDocumentationPublic relationsOfficerContext (archaeology)Political scienceBusinessPublic administration

Abstract

fetched live from OpenAlex

The purpose of this study was to assess the effectiveness of Sport Canada's Sport Funding and Accountability Framework (SFAF) from 1995–2004 within the context of four differently funded NSOs. The perceptions of 16 stakeholders (management staff, President, Sport Program Officer, athlete reps) were attained using semi-structured open-ended interviews. Accountability, accountability relationships, and the effectiveness of the SFAF relative to NSO development were the areas of focus. Written documentation verified information expressed by interviewees. Differences in internal accountability processes and the quality of the accountability relationships shared with Sport Canada existed within NSOs relative to its funding category. The SFAF was deemed an effective tool, but negative implications such as competition between NSOs and a tendency towards accountancy were noted. Recommendations for NSOs and Sport Canada regarding accountability in Canadian sport were developed.

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)0.042
metaresearch head score (Gemma)0.070
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Incentives · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.988
Threshold uncertainty score0.908

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0420.070
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0050.006
Science and technology studies0.0120.003
Scholarly communication0.0070.002
Open science0.0020.003
Research integrity0.0010.002
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 head0.017
GPT teacher head0.330
Teacher spread0.314 · 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.

Study designObservational
DomainIncentives
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

Citations28
Published2007
Admission routes2
Has abstractyes

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