Human genetic research, race, ethnicity and the labeling of populations: recommendations based on an interdisciplinary workshop in Japan
Bibliographic record
Abstract
BACKGROUND: A challenge in human genome research is how to describe the populations being studied. The use of improper and/or imprecise terms has the potential to both generate and reinforce prejudices and to diminish the clinical value of the research. The issue of population descriptors has not attracted enough academic attention outside North America and Europe. In January 2012, we held a two-day workshop, the first of its kind in Japan, to engage in interdisciplinary dialogue between scholars in the humanities, social sciences, medical sciences, and genetics to begin an ongoing discussion of the social and ethical issues associated with population descriptors. DISCUSSION: Through the interdisciplinary dialogue, we confirmed that the issue of race, ethnicity and genetic research has not been extensively discussed in certain Asian communities and other regions. We have found, for example, the continued use of the problematic term, "Mongoloid" or continental terms such as "European," "African," and "Asian," as population descriptors in genetic studies. We, therefore, introduce guidelines for reporting human genetic studies aimed at scientists and researchers in these regions. CONCLUSION: We need to anticipate the various potential social and ethical problems entailed in population descriptors. Scientists have a social responsibility to convey their research findings outside of their communities as accurately as possible, and to consider how the public may perceive and respond to the descriptors that appear in research papers and media articles.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".