Bibliographic record
Abstract
This essay is about the history of schooling in Western Canada for Indigenous peoples, non- British and non-English-speaking White settlers, and Chinese Canadians and Japanese Canadians. It brings their different schooling experiences into conversation with one another using an anti-racist approach to history. Different racisms, the essay argues, determined the different policies governments used to school these groups in Western Canada, which contributed to different racializations of the groups. Yet Indigenous peoples, non-British and non-English-speaking White settlers, and Chinese Canadians and Japanese Canadians believed schooling was the best chance the next generation had to access skills and knowledge that would help them thrive in Canada, a society they recognized treated their children as inferior. Racist school policies at many times obstructed their children’s access to schooling and the opportunities it provided. Policies also circumscribed to different degrees their chances to protect their languages and cultures in schools. An anti-racist history of this topic exposes racial inequalities at the heart of settler societies, and schooling, at a critical juncture in their development. Anti-racist histories also help teachers, teacher education students, and the public to understand that racisms are plural; how racialization occurs and confers privileges and disadvantages in the past and present; and Canadians are treaty people with obligations, particularly in education. This history helps to unpack racist and colonial baggage and to see better the journey ahead to a different and just educational future.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.027 | 0.015 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".