The Apologia Canadiana lessons for an Indian Boarding School Apologia Americana
Bibliographic record
Abstract
There are over 100 descendants of my grandmother Belva, my mother June and my older sisters Frances, Lynda, and Lillian. Indian residential schools affected all these women’s lives. Some descendants made their way in the world. Our family has artists, social activists, professionals and business people. We also have members who are part of the casualty list found in Statistics Canada 2006 and in the National Aboriginal Youth Strategy (NAYS 1999) . So, we also have sick people, people in jail, poor people, and people with addiction problems. Whether my relatives went to residential school and on-reserve schools, we all received an Indian Affairs education that tried to extinguish the Indian in us. Indian residential schools and on-reserve schools were imbued with assimilation goals. The mission to “kill the Indian in the child” remains the dark proclaimed maxim of Indian extinction called by Canada’s superintendent of Indian Affairs in the early 1900s. Indian education manuals issued by the federal Department of Indian Affairs instructed teachers to cultivate obedience to authority. Sometimes handled with care, Indian children were most often subjected to abuses by teachers of obedience. These abuses were acknowledged by the Government of Canada in its June 11, 2008 apology along with their pledge for financial redress.
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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.041 | 0.008 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.014 |
| Insufficient payload (model declined to judge) | 0.030 | 0.004 |
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".