Regions and Provincial Sport Organizations in Ontario, Canada: A Case Study
Bibliographic record
Abstract
Canada is a large country with a complex political landscape that has evolved over time. Regionalism has been vital in shaping Canada's political and economic development since its inception. Although the implications of regionalism are widely discussed in fields such as economic geography and rural development, little discussion of regions and regionalism (in Canada or abroad) has taken place related to sport policy and management. To date, much of the work in sport management in Canada has been focused on sport at either the national, or community level. As a result of this, much of the established literature to date does not touch upon provincial sport organizations (PSOs), particularly in relation to the understanding of regions and the spaces that fundamentally constitute these organizations. To address this gap, I analyzed how regions are understood and managed by actors within the field. The purpose of this study was to investigate the theoretical and practical implications of regions within sport governance in Ontario, Canada. This research answers the following key questions: 1) how do PSO-affiliated actors understand and construct regions? and 2) how do institutional pressures impact the management of regions within PSOs in Ontario? To answer these questions, an instrumental case study methodology was used to explore these questions within the province of Ontario. Data were collected through document analysis of organizational strategic plans and semi-structured interviews with decision-makers within PSOs. Thematic analysis (TA) was utilized in the analysis of data for this thesis. The dual frameworks of institutional theory and theories of space were utilized as the theoretical backdrop for analysis. Through analysis, three themes were identified in relation to how PSO-affiliated actors understand and construct regions: Recognition of the Province as a Region; Regions are Informally Constructed; and Regions are Formally Structured. This research highlights that regions are understood and managed differently by actors within PSOs, and that institutional pressures (coercive, normative, mimetic) impact organizations differently and ultimately contribute to this understanding and management. This work contributes to the sport management literature through an exploration of how space is constructed, understood, and managed by actors within an institutionalized environment.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.017 | 0.005 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".