The measure of a fan: Social patterns of voracious sports following in Canada
Bibliographic record
Abstract
Sociological studies on the manifestations and reproduction of inequality through cultural consumption have focused on few domains of culture and have mostly neglected intensity in consumption. Using large-scale survey data about professional sports following in Canada, we investigate how socioeconomic position is associated with intensity of professional sports following ("voraciousness"). Our multinomial logistic regression analyses suggest that social class, gender, and geography are predictors of voraciousness in each of the major professional sports leagues. Our latent class analysis (LCA) reveals seven sports following profiles marked by differences in range and intensity of following. We find that the most voracious sport followers are also the most omnivorous but are not distinctively the most privileged group. Findings suggest a distinct sport following profile of the predominantly male and economically dominant group in the field of social classes and point toward groups of sport followers with different styles of aversion and passing knowledge, perhaps in a type of deployment of Bourdieu's (1984) disinterested aesthetic. Overall, however, gender and region appear to be stronger predictors of voraciousness in sports following than social class in the Canadian context.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".