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The Perceived Impacts of the Rio 2016 Paralympics on the Lives of Disabled Brazilians

2024· preprint· en· W4403503694 on OpenAlexaff
Lyusyena Kirakosyan

Bibliographic record

VenuePreprints.org · 2024
Typepreprint
Languageen
FieldSocial Sciences
TopicInclusion and Disability in Education and Sport
Canadian institutionsInstitute on Governance
Fundersnot available
KeywordsPsychologyPolitical science

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.076
Threshold uncertainty score0.152

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0050.007
Scholarly communication0.0030.001
Open science0.0010.006
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.094
GPT teacher head0.402
Teacher spread0.308 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2024
Admission routes1
Has abstractyes

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