The Whiteness of Redmen: Indigenous Mascots, Social media and an Antiracist Intervention
Bibliographic record
Abstract
Resistance to the use of Indigenous themed mascots in North America has taken a variety of forms over the past several decades. This paper describes and analyses how a new vehicle for resistance, social media, can be integral to dismantling and eradicating racist images of Indigenous peoples. Specifically, this paper focusses on one campaign that questioned a high school sports mascot and team named the “Redmen”. By using examples from social media, the authors demonstrate how White settlers came to rely on the mascot imagery as a way to position themselves as superior and to regulate representations of Indigeneity. The authors’ analysis posits that the mascot is in itself a form of racialised colonial violence and they discuss how the name and mascot were protected by and through white settler surveillance and control. To intervene in this discourse of superiority and regulation, the paper describes how an anti-racist approach was used to design a social media campaign that built mass critical consciousness and a network of support within the community. The social media campaign coincided with and rallied support from the grassroots Indigenous Movement, Idle No More. The larger joint effort strategically and effectively redirected the public and critical focus to how the “Redmen” name and logo and other racist Indigenous mascots become normalised. Increased knowledge via social media catalysed a shift in public opinion which ultimately leads to retirement of the team name, logo and mascot.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.019 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.013 | 0.058 |
| Open science | 0.003 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".