JOGOS PARAPAN-AMERICANOS DE TORONTO 2015: um estudo da cobertura fotográfica no instagram do Comitê Paralímpico Brasileiro
Bibliographic record
Abstract
Neste estudo buscamos compreender de que maneira os enquadramentos fotograficos das postagens do CPB no instagram, que retrataram os atletas com deficiencia durante os Jogos Parapan-americanos de 2015, contribuiram para uma representacao esportivizada dos mesmos? Para a realizacao desta pesquisa, coletamos as postagens feitas no instagram do CPB do dia 31 de julho ao dia 15 de agosto de 2015, periodo de realizacao dos Jogos Parapan-americanos de Toronto 2015. Para o corpus de analise selecionamos 93 imagens que retratavam os atletas com deficiencia. As imagens foram organizadas e analisadas a partir de quatro categorias pre-definidas: 1) a visibilidade da deficiencia; 2) o espaco ocupado pelos atletas; 3) a postura dos atletas; e 4) a indumentaria dos atletas. Constatamos, ao final deste estudo, que a cobertura fotografica do CPB em seu perfil do instagram contribuiu parcialmente para uma visao esportivizada dos atletas com deficiencia, evidenciando uma tendencia em enfatiza-los ocultando as suas deficiencias, mostrando-os em posicoes passivas e sem os seus uniformes de competicao. Indicamos, portanto, que as instituicoes esportivas e midiaticas mostrem os atletas com deficiencia sem ocultar ou mitificar suas identidades corporais, sem criar uma identidade virtual ou uma segunda realidade sobre os mesmos.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".