Bibliographic record
Abstract
y studying treaty history began when an Indigenous student in one of my first postsecondary classes remarked that "everything that has gone wrong between Indigenous and non-Indigenous peoples can be traced back to the treaties."His observation was a well-known teaching of his grandfather, and, while the other Indigenous students in the classroom nodded sagely, I was shocked, stunned.As a non-Indigenous person who had grown up in Saskatoon, I had heard little, if anything, of the numbered treaties.Given all of my education, and my engagement with the world, how was this possible?I resolved right then to begin to learn everything that I could about the treaties in Canada.This resolve soon took me to the Office of the Treaty Commissioner in Saskatoon, where I was fortunate to work and even more fortunate to meet and come to know several Elders who are experts on our treaties.After I had worked for the treaty commissioner for a few years, I became comfortable speaking with all of the Elders who visited the office-all except Gordon Oakes.Although small in stature, he was intimidating and known to speak only Cree.One morning I arrived at the office early, and Elder Oakes was waiting at the front door.He was waiting to perform the Sacred Pipe Ceremony for the Treaty Table, but the meeting had been cancelled.When I gave him the news, he was crestfallen.His shoulders sagged, and his disappointment was immense.I drove him back to his hotel, but other than thanking me for the ride he did not say a word.I had heard
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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 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.219 | 0.125 |
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".