Navigating the Ethical Landscape of Medical Genomics: An Interview with Dr. Françoise Baylis
Bibliographic record
Abstract
Françoise Baylis CM, ONS, PhD, FRSC, FISC is a leading philosopher and bioethics expert, renowned for her pioneering research at the intersection of healthcare ethics, policy, and practice. She has shaped global gene editing standards through her work with the WHO and serves on the International Science Council's Governing Board. As Distinguished Research Professor Emerita at Dalhousie University, Baylis is dedicated to advocating for a more ethical and inclusive approach to science and biotechnology. Her work challenges conventional bioethics, pushing for broader, deeper thinking on health, science, and public policy. Baylis is a member of the Order of Canada and the Order of Nova Scotia, and an elected Fellow of the Royal Society of Canada and the International Science Council. She received the Queen Elizabeth II Platinum Jubilee Medal in 2022. That same year she was awarded the Killam Prize for the Humanities, followed by the Canada Council for the Arts Molson Prize in Humanities in 2023 – Canada's highest honours for humanities scholars. She is also the author of the award-winning book Altered Inheritance: CRISPR and the Ethics of Human Genome Editing, a critical guide to the ethical issues surrounding genome editing. In November 2024, Baylis was elected President of the RSC for a three-year term beginning in 2025. For more information about Françoise Baylis’ work, visit her website www.francoisebaylis.ca
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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 | 0.038 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.030 | 0.025 |
| Scholarly communication | 0.011 | 0.010 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.018 | 0.050 |
| Insufficient payload (model declined to judge) | 0.003 | 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".