Native Canadian Education Past and Present
Bibliographic record
Abstract
“American Indian and Native Canadian communities need skilled members if they are \nto survive and thrive,” asserts Keith James (James 2001). That’s true for all minority groups \nall around the world. At the same time minority groups have their education problems in each \ncountry. When I started to learn about First Nations education in Canada I only knew that \nCanada is one of the best-working multicultural countries in the world that has educational \nprojects for all means of education and all groups of students. I have been eager to know how \nAboriginal education projects help students fight difficulties. As some experts, for instance \nSabrina E. Redwing Saunders and Susan M. Hill state in their study, for a long time \nAboriginal education was only a “tool” of dealing with the “Indian Problem” (Saunders and \nHill 2007). They state “For centuries Canadian First Nations education has been a \nsubstandard, abusive means of dealing with the “Indian Problem”. We cannot neglect these \ncenturies if we want to see the sequence of development that characterizes the Aboriginal \neducation in Canada.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.029 | 0.003 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.095 | 0.011 |
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".