Cultivating ignorance of Aboriginal realities
Bibliographic record
Abstract
The principal problem in Aboriginal education in Canada is the education of Canadians. This article exposes Canada's long history of ignorance of Aboriginal Peoples and suggests that while education may not be the source of ignorance, it is now perpetuating it. Using the Ontario secondary school curriculum as an example, this article looks at mainstream Canadian and World Studies, of which geography is an integral part, and Native Studies courses, offered in Ontario since 1999, but available for study to few young Ontarians. Curricular reforms during recent decades have removed the worst expressions of racism, but have not addressed fundamental colonial attitudes in the mainstream curriculum. As a citizenry we are complacent about a deep‐seated ignorance of the country's past and present, affecting both Aboriginal and non‐Aboriginal Canadians. Lack of interest in traditional and modern Aboriginal cultures doom immigrants and established settlers to a dysfunctional relationship with the growing and increasingly internationally recognized indigenous population. As university educators and teachers of teachers, geographers must assume responsibility for promoting truthful and inclusive perceptions of Aboriginal Peoples in Canada and, in recognizing the subtle strategies of cultivating ignorance, examine how geography as it is currently taught in schools might exclude Aboriginal People and understanding .
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.026 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".