Conceptualizing the public good for genomics in the global South: a cross-disciplinary roundtable dialogue
Bibliographic record
Abstract
Since the Human Genome Project, initiatives to genetically sequence and profile populations around the world have expanded rapidly. The rationales guiding this expansion are diverse: on the one hand, the concentration of genetic technologies in the global North threatens to widen the yawning gaps in healthcare available in advanced versus developing nations. On the other, more 'genetic diversity' in global databases can reveal new points of genetic variation associated with health or disease. This promises to pave the way to a more personalized medicine of the future-more powerful and prosperous, with tailored prevention regimens and genetic treatments targeted to every individual's specific genetic vulnerabilities. These rationales are advanced to claim a public good case for genomics. However, the expansion of genomics to underserved populations in the global South has provoked many sociopolitical and ethical challenges. Critics have pointed to the inevitable entanglement of genomics with private commercial interests. These concerns are overlaid on deeper anxieties stemming from global asymmetries in scientific and technological power, and historical patterns of value extraction from colonized and marginalized populations. How then do we disentangle the public good? How do we build a genomics science that is just and equitable for the vast majority of the world? This conversation convenes leading genomics practitioners and critical science studies scholars to address these questions. We draw on an ongoing transdisciplinary dialogue, integrating the natural and social sciences, and bring together perspectives and scholars from the global North and South. Our aim is to cultivate a more holistic and grounded engagement with the scientific and political challenges we face, to truly understand the requirements of a genomics that centers the question of justice.
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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 | 0.059 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.029 | 0.138 |
| Scholarly communication | 0.035 | 0.051 |
| Open science | 0.004 | 0.027 |
| Research integrity | 0.022 | 0.032 |
| Insufficient payload (model declined to judge) | 0.006 | 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".