Ethical issues of CRISPR technology and gene editing through the lens of solidarity
Bibliographic record
Abstract
Background: The avalanche of commentaries on CRISPR-Cas9 technology, a bacterial immune system modified to recognize any short DNA sequence, cut it out, and insert a new one, has rekindled hopes for gene therapy and other applications and raised criticisms of engineering genes in future generations. Sources of data: This discussion draws on articles that emphasize ethics, identified partly through PubMed and Google, 2014-2016. Areas of agreement: CRISPR-Cas9 has taken the pace and prospects for genetic discovery and applications to a high level, stoking anticipation for somatic gene engineering to help patients. We support a moratorium on germ line manipulation. Areas of controversy: We place increased emphasis on the principle of solidarity and the public good. The genetic bases of some diseases are not thoroughly addressable with CRISPR-Cas9. We see no new ethical issues, compared with gene therapy and genetic engineering in general, apart from the explosive rate of findings. Other controversies include eugenics, patentability and unrealistic expectations of professionals and the public. Growing points: Biggest issues are the void of research on human germ cell biology, the appropriate routes for oversight and transparency, and the scientific and ethical areas of reproductive medicine. Areas timely for developing research: The principle of genomic solidarity and priority on public good should be a lens for bringing clarity to CRISPR debates. The valid claim of genetic exceptionalism supports restraint on experimentation in human germ cells, given the trans-generational dangers and the knowledge gap in germ cell biology.
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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 | 0.135 | 0.201 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.010 | 0.109 |
| Scholarly communication | 0.023 | 0.027 |
| Open science | 0.004 | 0.014 |
| Research integrity | 0.024 | 0.031 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".