The Social Economy and a Special Event: Community Involvement in the Whitehorse 2007 Canada Winter Games
Bibliographic record
Abstract
This article is premised on the idea that our understanding of the social economy can be developed through an examination of community engagement in a special sporting event. It explores the extent to which hosting a special event in Whitehorse, Yukon provided involvement opportunities for community members, and it explores the related outcomes for individuals and the community. The particular nature and attributes of Whitehorse as a northern community and the nature of the event-the Canada Winter Games-set the context for how the community and its members engaged with the event. The research explored involvement with the event in order to come to an understanding of specific outcomes in Whitehorse, and in relation to special events in the social economy more generally. Findings indicate increasingly positive assessments of the Games' impacts and resident involvement in the event through support, attendance, and volunteering. The highest-ranked motivations for volunteering suggest a strong connection to the event, linked in with the opportunity to contribute to the wider community goals of hosting the event. The findings of the study reiterate the importance of considering the local context of the social economy when exploring its expression through a special event. The basis for this article is a longitudinal research project that includes surveys, focus groups, and interviews in the community, with an emphasis on event volunteers.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".