Advocating for Gender Equity in Sport: An Analysis of the Canadian Women and Sport She’s Got It All Campaign
Bibliographic record
Abstract
Despite increasing access to sport and exercise opportunities, girls and women in Canada continue to face gender disparity in sport participation. Several media campaigns have emerged to address this disparity and advocate for gender equity in sport. However, there is little understanding or evaluation of the content of these media campaigns. Informed by sport participation research, the She’s Got It All campaign was designed to highlight the challenges and intersecting disadvantages that girls and women face in sport. The purpose of the current study was to assess the textual and visual content of this campaign. The posters ( N = 48) were analyzed using inductive thematic analysis (text) and deductive content analysis (visual) to identify the characteristics of the images and the themes in the messages. Based on the thematic analysis, seven main themes pertaining to girls’ and women’s barriers to sport participation are identified including physiology, gendered social behaviors, intrapersonal beliefs, environmental contexts, stereotypes, female representation, and interpersonal support. Based on the content analysis, most of the models presented in the posters are perceived as White and average-sized adult women, with visible muscle definition, slightly or nonrevealing clothing, and performing an individual sport. The poster visual and text material seem to miss opportunities to highlight the experiences of girls and women identifying as lesbian, gay, bisexual, transgender, queer (or sometimes questioning), and others and those classified as lower socioeconomic status. These findings provide foundational information for future research and media campaign designed to target gender equity in sport.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".