Bibliographic record
Abstract
Despite high and rising consumption of women’s sport in the United States (U.S.), limited research has addressed the social factors that lead individuals to become consumers of girls’ and women’s sport. This study uses 2018-19 National Sports and Society Survey (NSASS) data to examine the frequencies and predictors of U.S. adults’ consumption of girls’ and women’s sport. We integrate social structure and life course theories to consider social structural locations, parental and own sports involvement during childhood, adult gender ideologies, and adult sport commitments as predictors of adults’ consumption of girls’ and women’s sport. The results indicate moderate consumption of female sport, with just over half of adults appearing to spend some time watching/following female sport in the last year. Also, we estimate that U.S. adults average watching/following female sport for about one hour per week, but they report that their watching/following of female sport is typically only a small fraction of their overall sport consumption. Regression findings show consistent evidence of sexuality, number of female family members, parents’ sports fandom or athleticism, childhood sports involvement, adult gender ideologies, and adult sport commitments shaping the consumption of female sport. In addition, there is some initial evidence that gender seems to encourage men to watch/follow more female sports, but women appear more prone to watch female sport as opposed to male sport. However, gender and sexuality interact with one another in predicting female sport consumption, such that gay/lesbian identities particularly heighten consumption of female sport among women. Consequently, there is evidence that lesbians are disproportionately among the most avid consumers of girls’ and women’s sport.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".