Ethics of DNA research on human remains: five globally applicable guidelines
Bibliographic record
Abstract
We are a group of archaeologists, anthropologists, curators and geneticists representing diverse global communities and 31 countries. All of us met in a virtual workshop dedicated to ethics in ancient DNA research held in November 2020. There was widespread agreement that globally applicable ethical guidelines are needed, but that recent recommendations grounded in discussion about research on human remains from North America are not always generalizable worldwide. Here we propose the following globally applicable guidelines, taking into consideration diverse contexts. These hold that: (1) researchers must ensure that all regulations were followed in the places where they work and from which the human remains derived; (2) researchers must prepare a detailed plan prior to beginning any study; (3) researchers must minimize damage to human remains; (4) researchers must ensure that data are made available following publication to allow critical re-examination of scientific findings; and (5) researchers must engage with other stakeholders from the beginning of a study and ensure respect and sensitivity to stakeholder perspectives. We commit to adhering to these guidelines and expect they will promote a high ethical standard in DNA research on human remains going forward.
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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 | 0.238 | 0.186 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.007 | 0.019 |
| Scholarly communication | 0.016 | 0.008 |
| Open science | 0.008 | 0.014 |
| Research integrity | 0.025 | 0.025 |
| Insufficient payload (model declined to judge) | 0.004 | 0.004 |
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".