Sport Delivery in a Highly Socialized Environment: A Case Study of Embeddedness
Bibliographic record
Abstract
The social embeddedness of economic interaction has emerged at the forefront of economic sociology over the last 15 years. In the context of sport, however, little research has been undertaken to enhance our understanding of how the socialized context surrounding sport organizers, local governments, and corporate sponsors impact decisions affecting sport delivery. Therefore, the purpose of this case study is to explore the social embeddedness of decision makers in sport organizations and the local government that shape sport delivery in one community. An embedded perspective of economic interactions considers the continuity of relationships that generate particular behaviors, norms, and expectations. In-depth interviews with the leaders of this community’s sport organizations and the members of its local government were undertaken to gain insight into the nature of how decisions pertaining to sport delivery were shaped and constrained by the social context in which they were bounded. The results of this research suggest that the informal interaction among community leaders in sport and politics served to inhibit change in the way sport programs were delivered in this community. Further, taken for granted assumptions of city leaders about the type, number, and quality of sports delivered to the residents resulted in fewer opportunities for sport participation, despite an awareness of the limitations of the existing programs.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.017 | 0.009 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.003 |
| 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".