The Perceived Impacts of the Rio 2016 Paralympics on the Lives of Disabled Brazilians
Bibliographic record
Abstract
The Paralympic movement leaders, sporting mega-event organizers, and para-athletes in Brazil and elsewhere made frequent claims about the potential of the Paralympics to raise disability rights awareness and generate change in society’s perceptions of disabled people. However, how disabled people themselves view the Paralympics and their outcomes is insufficiently explored in the media and academic literature. This article has a two-fold purpose: first, to explore the views and perceptions of disabled Brazilians regarding the societal change claims made about the Rio 2016 Paralympics; and second, to problematize these claims of lasting societal change through the lens of critical disability theory. The online qualitative survey conducted a year after the Rio mega-event explored the following issues: a) attitudes of broader society towards disabled people; b) disability sport as a tool for social inclusion and equality; c) para-athletes’ visibility and the broader challenges; d) images of disabled people in the Paralympic coverage; and e) outcome of media’s attention for disabled people in general. The main argument is that realizing the Paralympic legacy promises is more complex than the Paralympic movement leaders and the event organizers acknowledge and that the social change legacies are the responsibility of the larger community long after the Games are over.
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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 | 0.006 |
| 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 | 0.007 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| 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".