The Discussions around Precision Genetic Engineering: Role of and Impact on Disabled People
Bibliographic record
Abstract
Genetic researchers are advancing in their abilities to extract precise genetic information from biological and human entities bringing genetic research steps closer to accurately modifying genes of biological entities, including that of humans. In this analytical essay, we focus on the discussions about precision genetic intervention that have taken place since March 2015 as they pertain to disabled people. We focus on two areas; one being the role of disabled people in the recent gene editing discussions and the second being the utility of existing legal instruments. Within our first focus we address the following questions: (a) What is the visibility of disabled people in the gene-editing discussions that have taken place since March 2015? (b) What has been the impact of those discussions on disabled people? (c) Were social problems which disabled people face taken into account in those discussions; (d) How does the reality of engagement with disabled people in these discussions fit with science, technology and innovation governance discourses that ask for more stakeholder, bottom up and anticipatory involvement? Within our second focus we address the following questions: (a) What is the utility of the United Nations Convention on the Right of Persons with Disabilities (UNCRPD); and (b) What is the utility of existing legal instruments covering genetic interventions: for preventing negative social consequences of genetic engineering developments for disabled people. We argue that (a) the genetic engineering debates since March 2015 have portrayed disabled people dominantly through a medical lens; (b) that the governance of science, technology and innovation of genetic engineering including anticipatory governance and responsible innovation discourses has not yet engaged with the social impact of gene editing on disabled people; (c) that few scholars that focus on the social situation of disabled people are visible in the governance discussions of gene editing; and (d) that the utility of the UNCRPD and the investigated genetic-related legal instruments and international agreements to protect disabled people from negative consequences coming out of the gene editing discussions is unclear at the least.
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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 | 0.036 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.028 | 0.073 |
| Scholarly communication | 0.018 | 0.019 |
| Open science | 0.002 | 0.027 |
| Research integrity | 0.012 | 0.014 |
| 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".