Building a capacity for equity, diversity, and inclusion through research, practice, and collaboration
Bibliographic record
Abstract
Within Canada, individuals who participate in sports and/or physical activity have diverse backgrounds and lived experiences. As such, it is important for sport and exercise psychology scholars and practitioners to acknowledge this diversity and ensure their work is equitable and inclusive for all. In this presentation, we reflect on our personal experiences conducting diversity research and our efforts to advance equity, diversity, and inclusion in sport and exercise psychology. We begin the presentation by providing guidance for how to conduct diversity research. Specifically, we highlight the importance of accurately defining diversity and introduce methods for conceptualizing and measuring specific diversity characteristics. Second, we discuss how we bring equity into our practices as sport and exercise psychology practitioners. Drawing from our experiences working with high performance teams, we discuss how to collaborate with coaches and athletes to create equitable experiences and emphasize the importance of considering athletes' intersecting identities (e.g., age, ability, sexuality, ethnicity). Finally, we conclude the presentation by speaking about facilitating inclusion through collaboration. Specifically, we use our own collaborative relationships to illustrate how valuing deep-level similarities (e.g., values, work ethic) can make for more rewarding and gratifying professional and personal relationships among individuals who may have surface-level differences (e.g., ethnicity, gender). Taken together, it is our hope that this presentation encourages scholars/practitioners to reflect on their own work and promotes discussion about how to best advance equity, diversity, and inclusion in sport and exercise psychology.
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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 | 0.128 | 0.084 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.050 | 0.120 |
| Scholarly communication | 0.049 | 0.040 |
| Open science | 0.008 | 0.105 |
| Research integrity | 0.012 | 0.022 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".