The ‘swagger’ of a nation: insider/outsider Canadian identity and the Toronto Raptors’ ‘We the North’ campaign
Bibliographic record
Abstract
This paper analyzes the ‘We the North’ (2014) promotional campaign featuring the Toronto Raptors of the National Basketball Association league. We analyze the campaign’s primary advertisement, as well fans and journalists’ adoption of the term ‘We the North’, to examine how marketers’ predominant use of ‘Blackness’ constructs and commodifies the identity of the ‘outsider’ to audiences. The ad uses the urban streetscape to create a branded version of nationalism based on insider-outsider, outsider-insider narratives of belonging. Moreover, the appearance and deportment of Black bodies and Blackness are used to tell a particular story of ‘the north’. Members of the public incorporated these ideas into embodied, branded, and patriotic expressions of support for the Raptors. By centering the bodies of players and fans in our analysis, we demonstrate that ‘We the North’ nationalism risks obscuring the lived realities of anti-Black and anti-Indigenous racisms faced by many Canadians.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.025 | 0.017 |
| Scholarly communication | 0.009 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".