Bibliographic record
Abstract
This chapter focuses on social inclusion using the theoretical framework of social event leveraging. Socially including the individuals who are generally excluded from society through the organization of a sport event is posited as a community-based legacy, since some individuals in the community may be affected disproportionately more negatively by the event. The chapter presents case study evidence from two sport mega-events, namely the Vancouver 2010 Winter Games and the London 2012 Summer Games. This case study data contextualize the concept of social inclusion in a complex sport mega-event community setting. The chapter details community event stakeholders who are actors and beneficiaries in the process of social event leveraging. Leveraging for social inclusion invokes the idea that the sport event will benefit everyone in the host city by creating a sense of community. The chapter reflects on social event leveraging for social inclusion in the case of smaller community events and concludes by providing practical implications for community stakeholders.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.009 | 0.020 |
| Scholarly communication | 0.011 | 0.011 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.022 | 0.003 |
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".