#BodyIssue and Instagram: A Gender Disparity in Conversation, Coverage, and Content in ESPN The Magazine
Bibliographic record
Abstract
ESPN The Magazine’s The Body Issue positions itself as an inclusive and sport-focused publication. With a focus on gender, the purpose of the current study was to examine the online thoughts and opinions that resulted from #BodyIssue on Instagram. In addition, the Instagram posting activity of ESPN (@espn) and espnW (@espnw) as it pertained to the promotion of the featured athletes and the Instagram accounts of the athletes featured in the 2016 Body Issue were explored. A text and network analysis surrounding #BodyIssue for both male and female Body Issue athletes was conducted using the Netlytic program. Manual Instagram tracking of @espn and @espnw, as well as the featured athletes’ accounts, was performed. In its entirety, this study was conducted between June 29 and July 13, 2016. Online thoughts and opinions, although differing by gender, were generally positive, with a large focus on physical form, not sexuality and/or nudity. Furthermore, a gender disparity was reported in regard to ESPN Inc.’s Instagram posting activity, with @espn choosing only to celebrate its male Body Issue athletes on Instagram and @espnw only posting about 2 of the 9 female athletes. There was a significant difference in the number of Instagram followers for the female athletes 1 wk prior to the online release of the issue ( M = 105,767.78, SD = 141,193.71) and 1 wk postrelease ( M = 109,742.56, SD = 142,890.11), t (8) = −4.29, p = .003. Further analyses of other Body Issue editions is needed to continue investigating this gender disparity and its potential impact on athletes, sport culture, and social attitudes.
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 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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".