The ratio of vision to data: Promoting emergent science and technologies through promissory regulation, the case of the <scp>FDA</scp> and personalised medicine
Bibliographic record
Abstract
Abstract Pharmacogenetic tests provide genetic data to tailor drug treatment and were widely predicted to be one of the first fruits of the Human Genome Project. In the mid‐2000s, the US Food and Drugs Administration (FDA) became an advocate for pharmacogenetic testing, but its efforts to build a market for this new technology brought the agency into dispute with other regulatory actors over the type of evidence needed for the adoption of pharmacogenetic testing, in particular the importance of randomized control trials. The warfarin case highlights the tension between a new form of promissory regulation driven by future expectations and FDA's established role as protector of public health; and the controversy can be conceptualized as a struggle over regulatory epistemologies within a complex polycentric regulatory space. Our case study addresses two themes central to the burgeoning scholarship on the governance of emergent science and technologies (EST): the political economy of regulation, in particular the role that regulators play in creating markets for EST; and the epistemological politics of regulatory science, in particular the controversy that arises when regulators modify scientific standards to accommodate EST. Linking these two themes is the concept of promissory regulation: the idea that regulatory policy may be shaped by an institutional commitment to the transformational potential of EST. This concept sheds new light on the neo‐mercantilist nature of contemporary regulatory capitalism.
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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 | 0.072 | 0.063 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.017 | 0.095 |
| Scholarly communication | 0.023 | 0.016 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.016 | 0.017 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".