Decolonizing Sports Sociology is a “Verb not a Noun”: Indigenizing Our Way to Reconciliation and Inclusion in the 21st Century? Alan Ingham Memorial Lecture
Bibliographic record
Abstract
In this paper, which is a revised and modified version of the 2019 North American Society for the Sociology of Sport Alan Ingham Memorial lecture, the author shares four views, contributions, and opportunities that sports sociologists might consider useful in how to decolonize as well as indigenize our discipline together. The need to actively engage in the theory and practice of how to decolonize while understanding what it also means to work toward becoming an accomplice, activist, ally, or co-resistor are important threads underpinning the nature and scope of this paper. The author concludes with a plea to sports sociologists that decolonizing our minds is as much a collective effort as it is an act of reconciliation while maintaining the promise of inclusion, equity, and human rights. As sports sociologists, understanding what it means to be in “good relations” with Indigenous Peoples is fundamental to how we continue to build on and improve our discipline together.
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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 | 0.008 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.039 |
| Scholarly communication | 0.010 | 0.012 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".