(Not So) Lost in Translation: Considering the GA4GH Diversity in Datasets Policy in the Japanese Context
Bibliographic record
Abstract
The genomics community has long acknowledged the lack of diversity in datasets used for research, prompting various stakeholders to confront this issue. In response, the Global Alliance for Genomics and Health (GA4GH) formulated a policy framework that recognizes the multiplicity of perspectives on diversity and proposed a systemic approach for more optimal data diversity. Given the importance of the research context, assessing this policy's applicability within countries where diversity is less discussed is important. This study investigated the feasibility of implementing the GA4GH policy in Japan, a nation with a smaller genetic diversity than many Western countries. As the proportion of East Asian genomic research is limited internationally, focusing on the Japanese genome contributes to enhancing diversity. Meanwhile, labelling findings as "Japanese" can inadvertently reinforce perceptions of homogeneity and overlook ethnic minorities. Regions and socioeconomic status are also recognized as substantial factors of diversity within academia, yet concerns persist among the public regarding the heritability of stigmatized conditions. Social inclusion of sexual minorities has begun in Japan, but research surveys generally still use binary sex and gender categories, which underscores the need for additional variables. This study found that both academia and the public need to confront the overemphasis on homogeneity within Japanese society and hesitancy in addressing genetic factors. By doing so, more inclusive and diverse datasets can advance the field both ethically and scientifically. Perhaps the most important impact of the GA4GH policy will be to draw greater attention to the complex diversity challenges ahead in Japan.
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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 | 0.166 | 0.218 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.014 | 0.016 |
| Scholarly communication | 0.021 | 0.023 |
| Open science | 0.004 | 0.019 |
| Research integrity | 0.020 | 0.019 |
| Insufficient payload (model declined to judge) | 0.010 | 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".