The nonsense of Native American sports imagery: Reclaiming a past that never was. International Review for the Sociology of Sport
Bibliographic record
Abstract
Abstract The pejorative use of Aboriginal imagery in professional and amateur sport has been criticized by Native Americans, public activists and academics from various disciplinary back-grounds. They have campaigned successfully to convince sport organizations to rid themselves of such slanderous signification. While sensitive to these activist and academic strategies such perspec-tives do not seriously take into account that many First Nations peoples and sport teams in Canada display these images in a variety of ways. The proliferation of these images in First Nations contexts motivated me to examine the issue of Native American sport imagery further, in particular how their use by First Nations peoples may problematize standard oppositional discourse that dominates academic literature on the subject. Through my interaction with these context specific uses of such imagery, I have come to formulate a reading that encourages the proliferation of these images, a call for their saturation in the sport and fashion industry. It is only then that their signifying potential of a reality that never was can be erased, giving way to new systems of meanings. Key words • ethnography • hockey • hyperreal • logos • mascots • Native American/First Nation In the past 20 years there have been concerted scholarly and public activist efforts to exorcise Native American imagery from the professional and amateur
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.017 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".