Shared Space: How North American Olympic Broadcasters Framed Gender on Instagram
Bibliographic record
Abstract
The Olympic Games offer scholars the opportunity to better understand how broadcasters visually frame male and female athletes to their large audiences. Traditionally, scholars have focused their efforts on the televised Olympic broadcasts and photojournalism coverage in newspaper and magazines. Scholarship has historically found that female athletes were underrepresented in event coverage and framed along gender stereotypes; however, in more recent Olympic Games, research has shown the news media has provided more equitable coverage between the genders. Yet digital and social media platforms (SMPs) play a significantly larger role in how Olympic broadcasters share content and engage with audiences. Utilizing media framing theory, this study examines how gender is framed on the Olympic Instagram accounts of the two official North American rights holders: the National Broadcasting Corporation (NBC) and the Canadian Broadcasting Corporation (CBC). Researchers collected a cross-sectional sample from the 2016 Summer Games in Rio de Janeiro, Brazil, and the 2018 Winter Games in Pyeongchang, South Korea. Results indicate that NBC and CBC were generally equitable in SMP coverage of men’s and women’s athletic achievements.
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 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 | 0.003 | 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 | 0.005 | 0.005 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".