Self-Representations of Women’s Sport Fandom on Instagram at the 2015 FIFA Women’s World Cup
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
The purpose of this study is to investigate how fans of women’s sport are using Instagram to self-represent their fandom. It uses the 2015 FIFA Women’s World Cup (WWC) as a case study to examine the ways in which fans at a women’s sport event express their fandom through images and to consider the social and political dimensions of using Instagram for promoting women’s sport. Instagram pictures containing the event-related hashtags #FIFAWWC, #LiveYourGoals, #SheBelieves, and #CanadaRed were collected over the tournament duration. From a content analysis of 3,605 images, the authors argue that visual networked platforms are facilitating online communication conventions within sport fan communities that function as forms of social presence to legitimate women’s participation as fans and generate visibility for women’s sport. By demonstrating that the production and sharing of visual content related to sport events have become important features of the contemporary sport fan experience, this article advocates for greater recognition of social media practices alongside conventional measures of sport fan engagement.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.003 | 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 it