What DNA can't tell: Problems with using genetic tests to determine the nationality of migrants
Bibliographic record
Abstract
This article relates to a set of recent reports about the Canadian Border Services Agency's (CBSA) use of commercial DNA ancestry tests to determine the nationality of detained migrants. While DNA tests are routinely used in many countries for the purposes of family reunification, these reports are particularly concerning. Not only do they imply a misunderstanding of the scope of genetics to shed light on legal and political phenomena such as nationality claims, but they also flag up important ethical problems regarding issues of consent and data privacy. In this article, the author clarifies the flawed logic behind using genetics to investigate nationality, outlines the ethical issues at stake and suggests amendments to existing norms in order to work towards more responsible practices in this area.
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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 | 0.171 | 0.321 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.010 | 0.051 |
| Scholarly communication | 0.011 | 0.011 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.010 | 0.012 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".