SPORTS IDENTITY OF THE ATHLETES WITH DISABILITY: A STUDY OF PHOTO COVERAGE ON INSTAGRAM OF THE BRAZILIAN PARALYMPIC COMMITTEE
Bibliographic record
Abstract
The media-sport speech can play a key role in the stigmatization or not stigmatization process of people with disability in society. In this sense, the main question of this study was: how photographic frames of CPB posts on instagram who depicted the disabled athletes during the 2015 Parapan-american Games did contribut to sports representation of the same? For this research, we collected all the posts made on instagram CPB of July 31 to August 15, 2015, period included the realization of the Parapanamerican Games to Toronto. For the analysis of corpus we selected 93 images that depicedt athletes with disabilities. The images were organized and analyzed from four predefined categories: 1) the visibility of disability; 2) the space occupied by athletes; 3) the position of the athletes; and 4) the clothing of athletes. We noted, at the end of this study, that the photographic coverage of the CPB in its instagram profile contributes partially for a sports view of disabled athletes, showing a tendency to emphasize them hiding their weaknesses, showing them in passive positions and without their competition uniforms. We noted, therefore, that sports and media institutions show athletes with disabilities without hiding or mystify their bodily identities without creating a virtual identity or a second reality about them.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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".