Emerging and evolving values in the changing landscape of genomics
Bibliographic record
Abstract
Recent advances in human genomics have transformed the field, leading to increased integration of genomics into mainstream clinical care, broadening the potential of personalized medicine, and expanding data generation and sharing. From the outset, genetics and genomics have given rise to a broad array of ethical concerns, including issues related to discrimination and stigmatization, informed consent, and reporting requirements of secondary findings. Ethics considerations and trends have evolved in parallel with the rapid technological progress in genomics. Like other transformative technologies, genomic innovations are governed by a combination of laws and ethics guidelines to ensure their responsible implementation. In this manuscript, we propose three key values that are crucial and timely to address now: equity, collective responsibility in the mainstreaming of genomics, and, sustainability. Equity warrants renewed attention due to its critical role in ensuring fair access to genomic innovations and promoting equality within society at large. Collective responsibility in the mainstreaming of genomics is equally important, especially as genomics becomes more broadly available in healthcare and to the broader public, thereby emphasizing shared accountability in its ethical application. Finally, in a context of scarcity of financial, personnel and environmental resources, sustainability needs to be considered to ensure the future of responsible governance in research and healthcare. The goal is to ensure equal access to genomic innovations, promote the ethically responsible use of genomic technologies, and support the long-term sustainability of the field.
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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 | 0.052 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.010 | 0.115 |
| Scholarly communication | 0.028 | 0.033 |
| Open science | 0.003 | 0.016 |
| Research integrity | 0.012 | 0.017 |
| Insufficient payload (model declined to judge) | 0.005 | 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".