Seeing is Believing: Special Olympics Events And the Society of the Spectacle
Bibliographic record
Abstract
The purpose of this research project is to better understand how one global sport for development agency takes advantage of events to build partnerships. This study demonstrates how the current social context, as theorized in Guy Debord's Society of the Spectacle, facilitates the implementation of what we label as a "seeing-is-believing" strategy. This strategy allows Special Olympics to capitalize on society's fascination with events to activate partners. Accordingly, a conceptual model that synthesizes and contrasts the aims of commercial spectator sports and sport for development events is provided. This model demonstrates that events are effective partnership-building arenas because, on one hand, they offer opportunities to efficiently evaluate mission attainment. These opportunities exploit our familiarity with events and the unthreatening passivity of watching. On the other hand, events provide pretexts for getting over the initial awkwardness sometimes associated with interacting with athletes identifying with intellectual disabilities. Theoretical and practical implications of the concepts that make the seeing is believing strategy work will also be provided.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.014 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".