Women and the blogosphere: Exploring feminist approaches to sport
Bibliographic record
Abstract
The notion of sports fandom is generally built on the ways men understand and relate to sport. In this research, we explore how women, who come together in an online place, define and understand sport with the goal of better understanding female fandom. Using Coakley’s ((2004) Sports in Society: Issues and Controversies. New York: McGraw-Hill) framework for conceptualizing sport and Lenskyj’s ((1994) Women, Sport, and Physical Activity: Selected Research Themes. Gloucester, ON, Canada: Sport Information Resource Centre for Sport Canada) feminist approaches to competition, we analyzed the profiles of women bloggers who write about sports in two online communities, BlogHer and Women Talk Sports, to examine their relationship to sport from a feminist perspective. The analysis suggests that women’s interest is predominantly reflected, not through consumption, but through participation. In addition, women in these networks complicated dominant ideologies about the role of sport as many of them considered participation and competition as a site for building connections and empowering other women. Finally, women who wrote about sport fandom engaged in the construction of “woman’s perspective” on men’s sports and in advocacy of women’s sports. We argue that these women bloggers offer an alternative approach and, thus, may challenge the masculine understanding of performance-oriented institutionalized sports.
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.
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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.006 | 0.023 |
| Scholarly communication | 0.009 | 0.012 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".