Falling giants and the rise of gene editing: ethics, private interests and the public good
Bibliographic record
Abstract
This paper considers the tensions created in genomic research by public and private for-profit ideals. Our intent is to strengthen the public good at a time when doing science is strongly motivated by market possibilities and opportunities. Focusing on the emergence of gene editing, and in particular CRISPR, we consider how commercialisation encourages hype and hope-a sense that only promise and idealism can achieve progress. At this rate, genomic research reinforces structures that promote, above all else, private interests, but that may attenuate conditions for the public good of science. In the first part, we situate genomics using the aphorism that 'on the shoulders of giants we see farther'; these giants are infrastructures and research cultures rather than individual 'heroes' of science. In this respect, private initiatives are not the only pivot for successful discovery, and indeed, fascination in those could impinge upon the fundamental role of public-supported discovery. To redress these circumstances, we define the extent to which progress presupposes research strategies that are for the public good. In the second part, we use a 'falling giant' narrative to illustrate the risks of over-indulging for-profit initiatives. We therefore offer a counterpoint to commercialised science, using three identifiable 'giants'-scientists, publics and cultures-to illustrate how the public good contributes to genomic discovery.
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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 | 0.017 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.089 |
| Scholarly communication | 0.015 | 0.015 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.008 | 0.011 |
| Insufficient payload (model declined to judge) | 0.003 | 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".