What is it about association football – the arrogantly self-appointed “Beautiful Game” – that renders most (though not all) of its fan cultures so ugly?
Bibliographic record
Abstract
It’s very simple, actually: The more important something is to any of us, the more passionate we are about that item. And with the high degree of our passion for this thing, there also emerges a high degree of our defending it at all costs, even that of resentment, taunting, hatred, exclusion, violence, death. This pertains to family, clan, tribe, nation – any experienced but also imagined community. This communal or tribal experience grounds our relationship to our beloved sports clubs. But here, too, there is a gradation of affect and self-identification that heightens our love and passion for “ours” and, concomitantly, our hatred and disdain for “theirs.” For reasons that I will explain in my paper, Association Football’s “ours” has developed more potently in societies in which this game has become culturally hegemonic since the late nineteenth century (almost solely in Latin America and Europe, later in much of Africa); than did other sports in these same societies; as well as sports that attained cultural hegemony elsewhere, most notably the North American countries of the United States and Canada; but also places like Australia and New Zealand; as well as India and Pakistan and China and Japan.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.011 |
| Scholarly communication | 0.015 | 0.008 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.014 | 0.004 |
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".