A Mixed Methods Systematic Review of the Ethical Issues Associated with the Use of Race, Ethnicity, and Genetic Ancestry
Bibliographic record
Abstract
The use of race, ethnicity, and genetic ancestry (RE/GA) in genomics research raises a plethora of ethical issues.Although there is abundant academic literature on this topic, currently no comprehensive and cohesive synthesis exists.This thesis uses a mixedmethods systematic literature review to address this knowledge gap, and explore three key research questions:1. What are the ethical issues associated with the use of race, ethnicity, and genetic ancestry in genomics research?2. How does an author's academic discipline aXect the types of ethical issues they discuss? How do the ethical challenges identified evolve with time between 2003 to 2023?Using traditional literature methods this review identified 298 peer-reviewed articles published from 2003 to 2023.Simultaneously, applying qualitative content analysis methods, each extracted article is labelled with one), a series of thematic codes that represent the most salient challenges an article engages with, two) the first and last authors' academic discipline, and three) the year of publication.This thesis is organized around two major sections.The first section attempts to answer the first research question by providing an in-depth exploration of ten thematic codes.Each thematic code represents a set of ethical issues associated with the use of RE/GA in genomics research.The second section aims to explore the relationship between thematic codes, authors' discipline, and the year of publication.This yielded three key findings.Firstly, some thematic codes exhibit temporal patterns, increasing or decreasing in frequency based on the year of publication.Secondly, authors demonstrate some discipline-specific tendencies and bias in the ethical issues they discuss.Lastly, the research topic shows multidisciplinary engagement, with high levels of contributions by authors of all discipline categories.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".