On whiskey, generational osmosis, and thinking ‘til it hurts: Eric Dunning and the Canadian Sociology of Sport Figuration
Bibliographic record
Abstract
Any study of violence in the sociology of sport owes a substantial debt of gratitude to Eric Dunning. This is certainly true of Canadian sociology of sport where, for at least three generations, the ideas of Dunning and his mentor, Norbert Elias, have proven both foundational and enduring for a core group of scholars. In this paper, two recognized scholars in the Canadian figuration, Michael Atkinson and Kevin Young, reflect on Dunning’s influence in shaping both their own and others’ understanding of how ‘sport matters’; in this case, the analysis of violence inside and outside of sport. The authors consider their own research ‘in the field’ of sport violence, their personal career intersections with figurational thinking, and both the formal and informal mentorship Eric provided over several decades. For both Michael and Kevin, Eric helped to shape not only the contents of their theoretical leanings over the course of time, but also instructed them to envision substantive issues related to sport violence as both long-term sociogenic and psychogenic processes more broadly. The paper concludes with personal reflections on Eric the scholar and gentleman.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.012 | 0.040 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".