Bibliographic record
Abstract
Over four decades of research has suggested that there is a high prevalence of homophobia and transphobic attitudes, behaviors, policies, and practices within sport and physical activity. These realities serve as barriers or deterrents to participation for many 2SLGBTQIA+ people and groups and can also make sport and physical activity unwelcoming and even unsafe for those who choose to participate. In this important body of work, there remains a glaring absence of racialized 2SLGBTQIA+ athletes’ experiences. To help to address this gap, the primary purpose of my research was to explore how marginalized communities experience overlapping forms of discrimination in sport. Adopting an autoethnographic methodological approach, I wrote a series of vignettes about my own experiences as a queer racialized athlete in Western Canada. While writing the vignettes was a reflective process in and of itself, I also followed the tenets of critical discourse analysis to think about my experiences in relation to the broader cultural context. This resulted in the creation of three themes: intersectionality, microaggressions, and homophobia. Each worked independently and together to create an unsafe space for me, impacting my athletic experiences and life more broadly in negative ways. Reflecting on my experiences critically, however, also allowed me to think about resistance and resiliency. My hope is that my work contributes to existing literature and provide insight for other queer racialized athletes who may have had similar experiences in sport.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.020 | 0.012 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".