Women's consumption of men's professional sport in Canada: Evidence of the ‘feminization’ of sports fandom and women as omnivorous sports consumers?
Bibliographic record
Abstract
Women sports fans have been substantially understudied compared to their male counterparts. While a growing number of studies seek to redress this, there remains a stark absence of quantitative approaches that would allow investigations regarding patterns of women’s sporting consumption and historical trends in the potential growth of this fandom. Using large-scale survey data from Canada from 1990 through to 2015, and employing quantitative methods of latent class and regression analysis, this study seeks to redress these issues by testing the ‘feminization’ thesis of increased women’s sporting fandom over the past three decades. In addition, we consider whether women’s fandom has become increasingly ‘omnivorous’ over this time period and the nature of this consumption today. Results show support for the feminization thesis. These findings are significant as through the use of quantitative methodologies we evidence the narrowing gender gaps in professional sports following between men and women, and women’s increasingly omnivorous consumption of sports. However, we find substantial gender gaps and inequalities in omnivorism by which the evidence suggests increased socio-economic and cultural barriers to omnivorous consumption of sport for women. We suggest that these women omnivores may be able to utilize their sporting knowledge in an instrumental way for benefits in various social settings, especially workplaces. It is hoped that this article will pave the way for further quantitative studies on women sports fans across different contexts.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".