Moving beyond Weiss and Springer’s<i>Repatriation and Erasing the Past:</i>Indigenous values, relationships, and research
Bibliographic record
Abstract
Abstract This commentary debunks the poor scholarship inRepatriation and Erasing the Pastby Elizabeth Weiss and James Springer. We show that modern bioarchaeological practice with Indigenous remains places ethics, partnership, and collaboration at the fore and that the authors’ misconstructed dichotomous fallacy between “objective science” and Indigenous knowledge and repatriation hinders the very argument they are espousing. We demonstrate that bioarchaeology, when conducted in collaboration with stakeholders, enriches research, with concepts and methodologies brought forward to address common questions, and builds a richer historical and archaeological context. As anthropologists, we need to acknowledge anti-Indigenous (and anti-Black) ideology and the insidious trauma and civil rights violations that have been afflicted and re-afflicted through Indigenous remains being illegally or unethically obtained, curated, transferred, and used for research and teaching in museums and universities. If we could go so far as to say that anything good has come out of this book, it has been the stimulation in countering these beliefs and developing and strengthening ethical approaches and standards in our field.
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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 | 0.010 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.042 |
| Scholarly communication | 0.013 | 0.015 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.003 | 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".