Introduction: Indigenous Re-Membering and Biopolitics in the Liberal Settler Colony
Bibliographic record
Abstract
A N D E R (née Henry) brought the white nurse to her sister's home on the reserve, hoping that her sister and brother-in-law would agree to participate in an interview.The nurse was researching Anishinaabe experiences of diabetes, and Mary was working for her as an interpreter.As they stood at the doorway, Mary's sister asked her in Anishinaabemowin, "How much is she paying you to help them destroy us?"Many years later, Mary shared this story with me, a white medical anthropologist, while working with me as an interpreter of Wabaseemoong Elders' oral histories.She offered this narrative as part of her ongoing effort to educate me about some Anishinaabeg's well-founded suspicions of well-intentioned white visitors to Northern reserve communities.Mary's sister's question also conveys an awareness that in liberal settler societies such as Canada, Indigenous well-being is a paradox.I argue that the seemingly irreconcilable perspectives of Mary 2Indigenous Healing as Paradox
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 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".