Exploring the determinants of women football players’ Instagram popularity
Bibliographic record
Abstract
The women’s football market has experienced significant growth over the past decade. Athletes are leveraging this expanding market to develop their personal brands, utilizing social media as a primary promotional channel. The current research explores the determinants of women football players’ Instagram following and engagement within the athlete brand ecosystem. The research focuses on three levels of influence: the team as a master brand, media, and the market. We follow a sequential QUANT → quant mixed-methods design. Study 1, employing negative binomial regression to model Instagram data, indicates a positive impact of account authentication and the team’s audience size on athlete following and engagement, yet a negative impact of joint branding by clubs (i.e. when men’s and women’s teams are branded on the same account). Study 2 delves deeper into the dynamics of teams’ branding to understand the sources of impact on athletes, employing quantitative content analysis. It uncovers inequitable branding practices exhibited by clubs that brand men’s and women’s teams jointly, explaining the hindering effects of such a practice on the women athletes’ social media popularity. This research contributes to sports brand scholarship, while also accounting for gender dynamics in clubs’ branding as a factor impacting athlete brands.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 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".