Once More, With Feeling: Sport, National Anthems, and the Collective Power of Affect
Bibliographic record
Abstract
In this paper I apply insights from Sport Studies, Indigenous Studies, Music Studies, and Feminist Cultural Studies to illuminate and theorize the cultural, material, and political affective salience of national anthems staged prior to sporting events. To do so I analyze two different cases: The Aboriginal musical trio Asani’s 2014 multi-lingual performance of “O Canada” prior to an Oilers hockey game which closed Truth and Reconciliation Commission (TRC) events in Edmonton, Alberta; and the projection of hatred onto former NFL quarterback Colin Kaepernick’s kneeling protest of racism during the playing of the U.S. national anthem in 2016. Analysis suggests that these emotive, often visceral musical performances and responses are not contained within individual subjects but instead reflect contextually specific repetitive (dis)articulations across time, space, and a variety of bodies. Placed within broader colonial contexts, Asani’s version of the Canadian anthem is exemplary of the embodied sensory, but politically limited settler-oriented communitas of Canadian TRC inclusionary music as previously explicated by Robinson. Kaepernick’s anti-racist kneeling activism provides an additional case to theorize the relationship of national anthems in regards to movements for and against an imagined white nation as well as State-sanctioned colonization and hatreds.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.026 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".