Bibliographic record
Abstract
This book was inspired by the hundreds of Aboriginal students who Greg taught in postsecondary programs in northern Alberta and northern BC in the 1990s and in Saskatchewan in the 2000s and 2010s.These students spoke frankly about the real challenges their communities faced.However, they were also committed to building self-reliant communities within Canada and were proud to be both First Nations or Métis and Canadian.Métis students in Grouard held tremendous pride in the achievements of the 1990 Métis Settlement Act, the first and only legislation of its kind in Canada, while Nisga'a students in Gitlaxt'aamiks (then New Aiyansh) took pride in the fact that their First Nation had made Canadian history by pioneering a third order of government.At that time, however, most books and academic research focused on the problems facing Aboriginal peoples in Canada.Students were perplexed.Even in the midst of difficult social problems and economic challenges, Aboriginal students saw resilient cultures and vibrant communities.Just as importantly, they rightly saw that Aboriginal people had made, and continue to make, significant contributions to the development of Canada as a nation.They often asked, "Why doesn't anyone talk about the positives of Aboriginal peoples?Why does everyone only focus on the problems?"From Treaty Peoples to Treaty Nation also originated over lunch with the Honourable Ron Irwin, then minister of Indian affairs and
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.374 | 0.246 |
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; the direct Gemma label and the distilled Codex classifier 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".