Vulnerable groups and the hollow promise of benefit from human gene editing
Bibliographic record
Abstract
Mainstream academic debate on the ethics of human gene editing is currently not as inclusive as it should be. For example, it currently does not give due consideration to Indigenous groups and cultures, such as those living in rural and remote areas of Canada. Once such people are given due consideration, then several important points emerge, which have so far gone unnoticed or under-emphasized in the debate. This article focuses on two of those points: (a) Some vulnerable people who are currently being ignored in the debate may not desire to use gene editing, even if it is safe, effective and affordable, and they will have compelling reasons for making this decision; and (b) even if such people do decide to use the technology, the gene editing enterprise itself is unlikely to do much good for them (and may even be harmful to them), as it alarmingly misses the point regarding the underlying contributing causes of the most pressing problems that those people are facing. Therefore, the promise of the gene editing enterprise is a hollow one for some groups of vulnerable people. These considerations should be used more prominently to guide debate on the ethics of human gene editing.
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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 | 0.044 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.077 |
| Scholarly communication | 0.013 | 0.014 |
| Open science | 0.002 | 0.018 |
| Research integrity | 0.021 | 0.021 |
| Insufficient payload (model declined to judge) | 0.004 | 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".