Balancing Biomedical Progress Against Reproductive Justice in the Case of Human Germline Genome Editing with CRISPR-Cas9
Bibliographic record
Abstract
CRISPR-Cas9, the Nobel-prize winning gene-editing technology, has been heralded as the biggest biotech discovery of the century. It touts the ability to one day effectively remove mutations from the human germline genome that cause genetic disease and disability. This is said to increase the quality of life of future generations. While CRISPR-Cas9 is often celebrated as the next frontier in genetic medicine, questions of its accuracy, which present important medical risks, is the biggest bioethical hurdle to its clinical utilization. However, there are many important socio-ethical implications of making heritable changes to the human genome that are marginalized from current debates. This is the premise of this thesis. I argue from a reproductive justice perspective that the promise of biomedical progress with CRISPR-Cas9 is misplaced and we are continuing to address social issues with technological solutions. Positive implications of the technology are far outweighed by its potential marginalizing social impact on women and disabled people. Through analyzing CRISPR-Cas9 regulation, which is highly influenced by a thin debate in public bioethics, I show how difficult it is to regulate emerging and transgressive technologies at a global scale and the troubling effects that uneven international regulation already has, seen through the rising trend of medical tourism. This leads me with two concluding questions: who benefits from biomedical progress and at what cost and what does it mean to ‘flourish’ within a social system that increasingly restricts notions of acceptable embodiment through biomedicine?
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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 | 0.024 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.018 | 0.072 |
| Scholarly communication | 0.017 | 0.016 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.022 | 0.022 |
| 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".