Two. Owning Death and Life Making “Indians” and “Eskimos” from Native Peoples
Bibliographic record
Abstract
t wo Owning Death and LifeMaking "Indians" and "Eskimos" from Native Peoples Questioning the Early Not-Yet-PastFor the issues that now confront the Native peoples of Labrador, history is not just what happened, nor how, nor even why.History in Labrador, as elsewhere, is primarily about pasts that are not past, pasts that still cause problems and, at the same time, are still used in trying to deal with present problems.There is nothing neat or simple about pasts that live openly, and at times confrontationally, in the present.This is often a special issue for Native peoples, who are often socially constructed in terms of what is, or is presumed to be, their history.Using a perspective on histories that people don't just "have" but live both within and against, we need to address three issues: (1) the production of dependency among formerly autonomous Native peoples; (2) the uses to which this dependency was put, by people who could pull the strings more or less effectively; and (3) the transformations that took place as Native peoples struggled within and against this dependency.At the center of all these issues is use-the ways that the hbc used, or tried to use, the dependence of the people they sought to shape into the In-
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.010 | 0.017 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".