CRISPR futures: Rethinking the politics of genome editing
Bibliographic record
Abstract
New genome editing techniques such as CRISPR-Cas9 aspire to automate and standardize laboratory practices of genetic engineering at the molecular scale. They have been promoted as a ‘revolutionary’ means of production, which will revitalize industry, transform agribusiness and adapt it to changing climatic conditions. To realize this vision, a fundamental regulatory shift is now being enacted by multiple national governments around the world from Argentina to Canada, Brazil, Australia, South Africa, the United States, the United Kingdom, Japan, China and the European Union. As corporate science is directly in the service of private entities guided by a strict market rationality, while public research is increasingly pushed to prioritize immediate ‘industrial applications’ and the achievement of measurable ‘socio-economic impact’, genomic interventions are mostly geared towards expanding , accelerating and securing the accumulation of capital on a global scale. Structural market demands are embodied in gene-edited bodies produced for commercialization. While the emerging international regulatory regime for gene-edited organisms has been largely shaped by discussions focused on technical questions of health and safety, this tendency indicates the necessity of a wider democratic debate that would include the socio-economic, ethical and ecological concerns recently stressed by indigenous and peasant movements around the world. How will these new GM bodies transform the way people live and work in agricultural lands, industrial facilities, barnyards and slaughterhouses, in biotech labs and medical clinics? How will they affect lived ecologies? What types of multi-species worlds are being constructed through bioengineering practices, by whom and according to what political visions?
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.027 | 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 | 0.006 | 0.044 |
| Scholarly communication | 0.013 | 0.018 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.009 | 0.016 |
| Insufficient payload (model declined to judge) | 0.008 | 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".