"A war without bombs" : the government's role in damming and flooding of Lac Des Mille Lacs First Nation
Bibliographic record
Abstract
de reproduire, publier, archiver, sauvegarder, conserver, transmettre au public par telecommunication ou par Nnternet, preter, distribuer et vendre des theses partout dans le monde, a des fins commerciales ou autres, sur support microforme, papier, electronique et/ou autres formats.L'auteur conserve la propriete du droit d'auteur et des droits moraux qui protege cette these.Ni la these ni des extraits substantiels de celle-ci ne doivent etre imprimes ou autrement reproduits sans son autorisation.In compliance with the Canadian Privacy Act some supporting forms may have been removed from this thesis.Conformement a la loi canadienne sur la protection de la vie privee, quelques formulaires secondaires ont ete enleves de cette these.While these forms may be included in the document page count, their removal does not represent any loss of content from the thesis.Bien que ces formulaires aient inclus dans la pagination, il n'y aura aucun contenu manquant.•+• Canada "The loss of land, and the loss of [wild] rice, and the loss of the livelihood that we had there, that I guess, and the loss of family connections, relationships, extended family, [it's] just almost like there was a war, we don't have those family connections.Yeah it's just, almost like there was a war... without bombs, but other ways, things happened.Yeah, it's almost like it was a war but without bombs and rifles, because families were taken away and they're separated and you don't know who and your land is devastated, things are lost, and you don't know whether you should turn left or right, and you have to go to a new place to live where you're not wanted there either, it's devastating, it's a catastrophe" Respected Elder Shirley Churchill Lac des Mille Lacs First Nation
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.016 | 0.008 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".