Bibliographic record
Abstract
This article examines one particular set of technologies arising from developments in human genetics, those aimed at improving the targeting, design and use of conventional small molecule drugs-pharmacogenetics. Much of the debate about the applications and consequences of pharmacogenetics has been highly speculative, since little or no working technology is yet on the market. This article provides a novel analysis of the development of pharmacogenetics, and the social and ethical issues it raises, based on the sociology of technological expectations. In particular, it outlines how two alternative visions for the development of the technology are being articulated and embedded in a range of heterogeneous discourses, artefacts, actor strategies and practices, including: competing scientific research agendas, experimental technologies, emerging industrial structures and new ethical discourses. Expectations of how pharmacogenetics might emerge in each of these arenas are actively shaping the trajectory of this nascent technology and its potential socio-economic consequences.
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 | 0.014 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.030 |
| Scholarly communication | 0.009 | 0.014 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.013 | 0.016 |
| Insufficient payload (model declined to judge) | 0.030 | 0.019 |
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".