Co-creating knowledge on bicycling: a decolonial feminist participatory action research approach to arts-based methods
Bibliographic record
Abstract
The colonising tendencies of Western research — in which Indigenous and racialised bodies are deliberately misrepresented — has justified the exploitation and violence towards these communities. Within the field of qualitative sport research, there is a need for research methodologies that relinquish power from the researcher, into the hands of the research communities . This paper aims to demonstrate the utility of a decolonial feminist participatory action research (PAR) approach to arts-based methods for sport research through an exploration of fieldwork with a Toronto-based bicycle organisation. A combination of data collection methods were used, including: 1) arts-based methods; 2) semi-structured interviews; and 3) reflexive journal notes. The results of this project demonstrated that a decolonial feminist PAR approach to arts-based methods can: 1) illuminate the non-human actors within art and bicycling; 2) help research colleagues critique systems of oppression; and 3) facilitate research colleague agency. Taken together, these findings demonstrate the importance of co-creating knowledge within sport scholarship to illuminate the diverse knowledges of those vulnerable to systemic oppression and erasure. This is a novel direction for challenging power relations within sport research and within sociological research more broadly.
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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 | 0.066 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.011 | 0.050 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.003 | 0.017 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".