Educational Legacy of the Rio 2016 Games: Lessons for Youth Engagement
Bibliographic record
Abstract
The promise of the Rio 2016 Games was to influence the entire population of Brazil, but the major impact was expected to be on children and the youth. The development of youth education programs promoting Olympic and Paralympic values was one of the main commitments that organizers made in 2009 to host the 2016 Olympic and Paralympic Games. This article draws on the available literature on Olympic and Paralympic education and youth engagement and examines several of such programs previously implemented in such host cities as Beijing, Vancouver, and London. The purpose was to explore the ways in which implementing such educational legacy programs by the Rio 2016 and other sporting mega-event organizers can inspire and sustain youth engagement. The inductive thematic analysis was applied in the close examination of the content, strategies, and outcomes of the Rio 2016 Olympic and Paralympic education program. The results suggest that to leave an enduring youth legacy, policymakers, future mega-event organizers, and educators need to understand it as a continued endeavor beyond the hosting period and embed the related educational efforts into broader educational and youth-focused structures. This article also outlines lessons for youth engagement that can be drawn from Rio’s and other host cities’ Olympic and Paralympic education practices.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.006 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".