Socio-economics of Personalized Medicine in Asia (Edition 1)
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The second decade of the twenty-first century has witnessed a surging interest in personalized medicine with the concomitant promise to enable more precise diagnosis and treatment of disease and illness, based upon an individual’s unique genetic makeup.In this book, my goal is to contribute to a growing body of literature on personalized medicine by tracing and analyzing how this field has blossomed in Asia. In so doing, I aim to illustrate how various social and economic forces shape the co-production of science and social order in global contexts. This book shows that there are inextricable transnational linkages between developing and developed countries and also provides a theoretically guided and empirically grounded understanding of the formation and usage of particular racial and ethnic human taxonomies in local, national and transnational settings.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.021 | 0.008 |
| Science and technology studies | 0.000 | 0.006 |
| Scholarly communication | 0.000 | 0.007 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.024 | 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 it