Community partnerships are fundamental to ethical ancient DNA research
Bibliographic record
Abstract
The ethics of the scientific study of Ancestors has long been debated by archaeologists, bioanthropologists, and, more recently, ancient DNA (aDNA) researchers. This article responds to the article “Ethics of DNA research on human remains: five globally applicable guidelines” published in 2021 in Nature by a large group of aDNA researchers and collaborators. We argue that these guidelines do not sufficiently consider the interests of community stakeholders, including descendant communities and communities with potential, but yet unestablished, ties to Ancestors. We focus on three main areas of concern with the guidelines. First is the false separation of “scientific” and “community” concerns and the consistent privileging of researcher perspectives over those of community members. Second, the commitment of the guidelines’ authors to open data ignores the principles and practice of Indigenous Data Sovereignty. Further, the authors argue that involving community members in decisions about publication and data sharing is unethical. We argue that excluding community perspectives on “ethical” grounds is convenient for researchers, but it is not, in fact, ethical. Third, we stress the risks of not consulting communities that have established or potential ties to Ancestors, using two recent examples from the literature. Ancient DNA researchers cannot focus on the lowest common denominator of research practice, the bare minimum that is legally necessary. Instead, they should be leading multidisciplinary efforts to create processes to ensure communities from all regions of the globe are identified and engaged in research that affects them. This will often present challenges, but we see these challenges as part of the research, rather than a distraction from the scientific endeavor. If a research team does not have the capacity to meaningfully engage communities, questions must be asked about the value and benefit of their research.
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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 | 0.196 | 0.186 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.019 | 0.085 |
| Scholarly communication | 0.021 | 0.024 |
| Open science | 0.005 | 0.031 |
| Research integrity | 0.023 | 0.028 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".