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Record W2912100996 · doi:10.5663/aps.v7i2.29334

A Comparison of Indigenous Sport for Development Policy Directives in Canada and Australia

2019· article· en· W2912100996 on OpenAlexaffvenueabout
Kevin Gardam, Audrey R. Giles, Steven Rynne, Lyndsay Hayhurst

Bibliographic record

Venueaboriginal policy studies · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicSport and Mega-Event Impacts
Canadian institutionsYork UniversityUniversity of OttawaLakehead University
FundersAustralian Government
KeywordsIndigenousGeneral partnershipPolicy developmentGovernment (linguistics)Political sciencePublic administrationPoliticsEconomic growthEconomics

Abstract

fetched live from OpenAlex

In this study, we employ Bacchi’s (2012) “What’s the Problem Represented to be” approach to guide our discourse analysis of federal Indigenous sport for development (SFD) policies in Canada and Australia. Through a review of government policies and reports, we highlight the often-divergent policy directives set out by federal departments in these two countries. Namely, inter-departmental partnerships in areas such as health, education, and justice fail to be adequately facilitated through SFD policies in Canada, while, conversely, Australia has strived towards greater federal partnership building. Within the identified Canadian and Australian policies, both countries consistently produced sport as having the potential to contribute to Indigenous peoples’ social and economic development, thus highlighting the growing institutional support behind Indigenous SFD. This policy analysis research provides a novel contribution to the overall growing body of literature investigating the politics of partnership building in SFD initiatives.

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)0.010
metaresearch head score (Gemma)0.022
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.171
Threshold uncertainty score0.961

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.022
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0040.008
Science and technology studies0.0180.009
Scholarly communication0.0080.002
Open science0.0020.005
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0030.000

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 head0.080
GPT teacher head0.473
Teacher spread0.393 · 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 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

Citations5
Published2019
Admission routes3
Has abstractyes

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