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Record W4415586811 · doi:10.21083/crrf.v36i1.8109

Provincial Sport Organizations’ Perspectives on Rural and Remote Communities in Ontario

2025· article· W4415586811 on OpenAlexaffabout
Kyle Rich, Dante Losardo

Bibliographic record

VenueProceedings of the Canadian Rural Revitalization Foundation · 2025
Typearticle
Language
FieldSocial Sciences
TopicSocial Sciences and Governance
Canadian institutionsBrock University
Fundersnot available
KeywordsViewpointsCorporate governanceThematic analysisRural areaRural developmentRural management

Abstract

fetched live from OpenAlex

Actors in Provincial Sport Organizations (PSOs) play an important role in the implementation of sport policy and the development of sport participation opportunities in diverse community contexts. In this study, we examine PSO actors’ understandings of rural and remote communities and how these understandings impact sport policy at the regional level. We employed an instrumental case study methodology. Data were collected through document analysis of organizational strategic plans and semi-structured interviews with 12 decision-makers from PSOs in Ontario. Data were then analyzed using thematic analysis. Preliminary findings indicate that while the needs of rural and remote communities were not necessarily reflected in the formal policies of PSOs, many organizations provided additional resources and training opportunities to support these regions. Additionally, actors from PSOs acknowledged and considered biases held towards rural/remote communities and the role that these biases played in policymaking, for example, related to hosting championships/events. By investigating the viewpoints of PSO actors, we shed light on how institutional factors impact the development of sport participation opportunities in rural and remote areas. Our research highlights the theoretical and practical implications of regions for sport governance within the province of Ontario, and how rural and remote communities are positioned within this organizational field.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.461
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0020.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.012
GPT teacher head0.257
Teacher spread0.245 · 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 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

Citations0
Published2025
Admission routes2
Has abstractyes

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