ESPN's #BodyIssue on Instagram: The Self-presentation of Women Athletes and Feedback from their Audience of Women
Bibliographic record
Abstract
This study used Instagram to explore the 2016 ESPN: The Magazine’s Body Issue, with a particular focus on the women athletes featured. A two-prong content analysis was utilized for this study. Photo analysis of “ESPN’s Body Issue photos” (i.e., released on ESPN’s website; N = 141) and “ESPN’s Body Issue photos posted on athlete’s Instagram” (i.e., ESPN photos posted on the athletes’ Instagram account; N = 16) was conducted. Most of “ESPN’s Body Issue photos” were “getting pretty” shots, whereas, the majority of “ESPN’s Body Issue photos posted on athlete’s Instagram” were “athletic action” or “active in sport.” Audience reactions from women to Body Issue photos posted on the women athletes’ Instagram accounts were explored through examining ~3,000 comments, and results suggest that women athletes can and do play a role in how other women socially construct themselves. Overall, findings contribute to understanding women athletes in the media.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".