Self-Representations of Women’s Sport Fandom on Instagram at the 2015 FIFA Women’s World Cup
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.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".