Bibliographic record
Abstract
The fields of biological anthropology and bioarchaeology have changed significantly over the past 30 years. Many non-Indigenous biological anthropologists are working collaboratively with communities on projects where analysis is being done on Ancestors and sacred places, providing information for Indigenous peoples about their past at their request, as demonstrated by the chapters in this volume. However, there are still significant barriers to transformative and sustained change in biological anthropology and bioarchaeology, as researchers continue to analyze Ancestors without permission, museums repatriate Ancestors yet retain their belongings, and settler colonial frameworks continue to define much anthropological practice. In this chapter, I evaluate how far the field has come and explore where we need to go next. Using the Truth and Reconciliation Commission of Canada’s Calls to Action as a starting place, I explore how research in Canada and other settler colonial contexts can shift the power to communities and build models of decolonial, Indigenous-led practice, not just collaboration. Using recent examples of cases where analysis of Ancestors had occurred without collaboration, I argue that we need to push further into the foundational structures of the discipline to advocate for lasting change.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.009 | 0.030 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".