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. RSUMCette note de recherche examine les tensions et les dfis auxquels notre quipe de recherche a t confronte en soulevant des questions sur les chercheurs blancs, en problmatisant la blanchit dans la recherche et en affrontant le racisme anti Noirs dans le sport.Nous documentons des ides importantes pour perturber la faon dont le privilge blanc est li la complicit dans le racisme.Ce faisant, nous rflchissons de manire critique au processus de notre travail avec un groupe de travail Black Lives Matter (BLM) et notre volution vers un alli proactif.Plus prcisment, nous orientons notre discussion sur les concepts de blanchit critique, d'humilit critique et d'inconfort en tant que chercheurs blancs impliqus dans un projet de justice sociale visant
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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 | 0.009 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".