Chapter 4 Good Medicine: Prescriptions for Indigenous Archaeological Practice
Bibliographic record
Abstract
ABSTRACT While the history of North American archaeology points to a long engagement with tribal elders and scholars, these encounters largely consist of unequal, extractive relationships wherein Indigenous collaborators and Indigenous archaeologists have been treated more as objects of study and pity—what Bea Medicine refers to as “creatures”—rather than as equal research partners. As an Indigenous woman and a settler Chicanx woman, we reflect on the life journey and scholarship of Bea Medicine, a Lakota scholar‐activist and mentor to Indigenous anthropologists. Dr. Medicine's work has provided generations of Indigenous anthropologists with the means to participate in the discipline with their whole selves and, importantly, on their own terms. We argue that Medicine's contributions provide good medicine for the field in the form of concrete strategies for continuing to decolonize the discipline and for doing work that centers the direct needs and perspectives of Indigenous peoples. When read alongside Indigenous archaeologies’ often overlooked grandmothers, mothers, and aunties, Medicine's work also highlights continuing disparities in archaeological practice, from our relationships with and to Indigenous nations to the relations we cultivate in the Academy as Black, Indigenous, and People of Color.
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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 | 0.027 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.021 | 0.118 |
| Scholarly communication | 0.016 | 0.011 |
| Open science | 0.004 | 0.011 |
| Research integrity | 0.012 | 0.019 |
| 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".