Chaco Canyon Dig Unearths Ethical Concerns
Bibliographic record
Abstract
The field of paleogenomics (the study of ancient genomes) is rapidly advancing, with more robust methods of isolating ancient DNA and increasing access to next-generation DNA sequencing technology. As these studies progress, many important ethical issues have emerged that should be considered when ancient Native American remains, whom we refer to as ancestors, are used in research. We highlight a 2017 article by Kennett et al., "Archaeogenomic evidence reveals prehistoric matrilineal dynasty," that brings to light several ethical issues that should be addressed in paleogenomics research. The study helps elucidate the matrilineal relationships in ancient Chacoan society through ancient DNA analysis. However, we, as Indigenous researchers and allies, raise ethical concerns with the study's scientific conclusions that can be problematic for Native American communities: (1) the lack of tribal consultation, (2) the use of culturally insensitive descriptions, and (3) the potential impact on marginalized groups. Further, we explore the limitations of the Native American Graves Protection and Repatriation Act, which addresses repatriation but not research, because clear ethical guidelines have not been established for research involving Native American ancestors, especially those deemed "culturally unaffiliated." Multiple studies of "culturally unaffiliated" remains have been initiated recently, so it is imperative that researchers consider the ethical ramifications of paleogenomics research. Past research indiscretions have created a history of mistrust and exploitation in many Native American communities. To promote ethical engagement of Native American communities in research, we therefore suggest careful attention to ethical considerations, strong tribal consultation requirements, and greater collaborations among museums, federal agencies, researchers, scientific journals, and granting agencies.
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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 | 0.028 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.012 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.010 | 0.019 |
| Insufficient payload (model declined to judge) | 0.009 | 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".