She's Got Game: An Exploration of the Athletic, Academic, and Social Experiences of Black Canadian Female U.S. Athletic Scholarship Recipients
Bibliographic record
Abstract
Much has been said about the academic, social, and athletic experiences of Black males, but very little attention has been paid to the ways in which the axis of gender, intersecting with race and class creates very specific experiences. Therefore, using Social Reproduction, Critical Race, and Black Feminist theories, this study explores the specific athletic, educational, and social experiences of Black female basketball athletes from the Greater Toronto Area (GTA) who have received U.S. athletic scholarships. \n \nDrawing on 20 semi-structured interviews with Black Canadian female scholarship recipients, between the ages 25 and 35, this study analyzes how they navigate athletic, academic, and social life to obtain U.S. athletic scholarships. Firstly, I find that the participants were socialized into the U.S. athletic scholarship pathway through a number of factors including social, familial, peer, and media influences. In addition, scholarship aspirations were also informed by negative schooling experiences and motivations like the avoidance of school debt and heightened athletic possibilities. Secondly, I find that once the participants were immersed in basketball, they relied on informal networks/communities of support to develop and share knowledge about scholarship opportunities to co-create complex and sometimes challenging pathways to American universities. Thirdly, I find that throughout this navigation, the participants endured, navigated, and resisted racial and gender stereotyping, identity projections, and gender and race-based barriers that were distinct from their non-Black female and Black male counterparts. Lastly, I highlight how while all the athletes successfully obtained athletic scholarships to American universities and benefitted from their experiences athletically and socioeconomically, their pathways were often arduous and precarious, rife with numerous drawbacks, risks, and sacrifices. In addition, I found that creating fulfilling and enriching academic and athletic opportunities and experiences was often perceived as unavailable and inaccessible in the Canadian context, resulting in the need for emigration. \n \nTherefore, I argue that it is not that Black youth lack the economic, social, and cultural capital to be successful athletically and academically, but that the rate of exchange for their capital shaped by historical scripts, systemically devalues the abundance of capitals they do possess and reproduces existing inequalities, failing to address the systemic nature of their exclusion.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.037 | 0.009 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.007 |
| 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".