Reading Between the Lines: Gender Stereotypes in Children’s Sport-Based Books
Bibliographic record
Abstract
A child’s first contact with media and culture typically comes from books they are exposed to in the home and at school. The narratives presented contribute to the early reinforcement of gender roles and norms and can greatly influence the way that young girls perceive and experience sport. The purpose of this study was to explore the narratives within sport-based books geared toward a young female audience to determine the extent to which they promote the engagement of girls in sport. A pragmatic literature search was conducted to obtain books that met our inclusion criteria. Books (n = 28) were analyzed based on the age of their intended audience (aged 3–5, 6–8, and 9–12 years) using thematic narrative analysis. Although the authors promoted the engagement of girls in sport, underlying gender stereotypes were nevertheless salient. Across the books, themes involved the emphasis of “feminine” sports as a context for diversity and learning, the need to understand development as a process, the importance of relationships, and implications pertaining to perceptions of capability as female athletes. Most importantly, the application of a critical feminist lens enabled us to identify an underlying theme—the reinforcement of gender stereotypes—that permeated the storylines and served to undermine the potential adaptive messaging intended by authors. These findings suggest the need for greater attention toward the complexity of female sport and a cultural shift in thinking toward gender equity rather than simply increasing sport access for female participants.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".