ESPN's #BodyIssue on Instagram: The Self-presentation of Women Athletes and Feedback from their Audience of Women
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it