Sculpting Pride: freeing the inner auntie in racialised queer qualitative sport research
Bibliographic record
Abstract
This paper is the retelling of a qualitative research journey that began with firm intentions and desires, but got lost and mired by the academic pressures and forgetting who the research was truly by and for: the racialised queer and gay men who are the heart of it. While they used their gym time to prepare their bodies for Pride, I expose researcher mess and disorientation. Through this, my deeper idea of an inner Auntie, a loving but firm voice of reason that aids racialised queer men to be the best versions of themselves, will be arising from the process of doing qualitative research. This phrasing then decentres Whiteness, distances itself from homonormativity, and acknowledges racialised queer culture and heritage. This new framing of inner Auntie also becomes a reflexive validation guide in doing qualitative research. Ultimately, the participants saw me through the disorientation, helped me reach a turning point, and oriented me towards my desires in how to be a better researcher. This is an introduction to other physical cultural researchers as an innovative way to conduct qualitative research in sport. It is also an offering of gratitude to the people I met in this research. May their lives continue to inspire others.
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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 | 0.124 | 0.086 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.029 | 0.074 |
| Scholarly communication | 0.017 | 0.015 |
| Open science | 0.003 | 0.021 |
| Research integrity | 0.004 | 0.009 |
| 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".