Bibliographic record
Abstract
ntil I read Magic Weapons I didn't realize that those of us who had attended residential school and then written about some of our experiences could cause such an uproar in the academic world so as to open the floodgates of the sea of deep thoughts and let loose a torrent of words.Reminded me of my father, Rufus's, astonishment when I told him of my newfound knowledge that I had gained upon joining the ranks of scholars at the Royal Ontario Museum in Toronto in 1970.I told him that I had learned that our Anishinaubae words fell into two categories, animate and inanimate; and that our verbs had a tense called "dubitative" that English didn't have.Dad stood, as if dazed, for some moments before remarking, "Gee Whitakers!I didn't know that we were that smart!"Like my father, I'm taken aback to learn that our words had such impact as to incite debates in the academic world.I didn't know that we, myself included, meant to heal, empower, and help people find their identities.If the works of Highway, Thrasher, Joe, and myself bring about these results, well and good.But I didn't have such lofty aims when I wrote Indian School Days.Mine were much more modest.It was simply to amuse the readers of The Ontario Indian, a magazine of the Union of Ontario Indians that ceased publication in the mid-1980s.After graduation from residential school in 1950, I and ten other former inmates of the school went to Wawa to work in the mines.There we formed a sort of community, often reliving some of our experiences while we were locked up in the Spanish school.For five summers I worked in Helen Mine, consorting with my old schoolmates; as always, we rehashed old memories.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.792 | 0.801 |
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".