A study of critical whiteness in sport research and disrupting racism: research with a Black Lives Matter task force
Bibliographic record
Abstract
This research note considers tensions and challenges faced by our research team in raising questions about white researchers, problematizing whiteness in research, and confronting anti-Black racism in sport.We document important insights to disrupt how white privilege is connected to complicity in racism.In doing so, we critically reflect on the process of our work with a Black Lives Matters (BLM) task force and our shift towards proactive allyship.Specifically, we frame our discussion through the concepts of critical whiteness, critical humility, and discomfort as white researchers involved in a social justice project confronting anti-Black racism in sport.Finally, with a call to action we identify the need for researchers to practice flexibility in research design and embrace social movements that alter the linear approaches to data collection and analysis.We also critique research team composition with our commitment to disrupting racism by problematizing whiteness. RÉSUMÉCette note de recherche examine les tensions et les défis auxquels notre équipe de recherche a été confrontée en soulevant des questions sur les chercheurs blancs, en problématisant la blanchité dans la recherche et en affrontant le racisme anti Noirs dans le sport.Nous documentons des idées importantes pour perturber la façon dont le privilège blanc est lié à la complicité dans le racisme.Ce faisant, nous réfléchissons de manière critique au processus de notre travail avec un groupe de travail Black Lives Matter (BLM) et à notre évolution vers un allié proactif.Plus précisément, nous orientons notre discussion sur les concepts de blanchité critique, d'humilité critique et d'inconfort en tant que chercheurs blancs impliqués dans un projet de justice sociale visant
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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 | 0.071 | 0.077 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.035 | 0.030 |
| Scholarly communication | 0.015 | 0.009 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 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".