Make the Indian Understand his Place: Politics and the Establishment of the Tom Longboat Awards at Indian Affairs and the Amateur Athletic Union of Canada
Bibliographic record
Abstract
The Tom Longboat Awards were created in 1951 through a joint agreement between the Indian Affairs Branch and the Amateur Athletic Union of Canada ostensibly to celebrate the life of Tom Longboat, the Onondaga runner who caught the world's attention in the early 1900s. His passing in 1949 sparked a trend in memorialising him that continues to this day. The Tom Longboat Awards, which have been given out every year since their inception, are the longest standing and most prestigious award for Native athletes in Canada. Using documents collected from the National Archives of Canada, online repositories, personal archives, as well as oral interview data, this paper examines the broader political context that gave rise to the Awards and the significance that was attached to them by Indian Affairs and the Amateur Athletic Union of Canada. This paper shows that the Tom Longboat Awards are much more than a simple celebration of athletic accomplishments. Rather, they were created to be a valuable tool through which to advance specific social and political agendas; in the 1950s, these agendas focused primarily on education, health, and citizenship, all of which were further embedded into a larger colonial environment aimed at Native assimilation.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.050 | 0.035 |
| Scholarly communication | 0.016 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".