Bibliographic record
Abstract
This chapter concerns about developing an analysis of the implications of the nanomedical paradigm. It reviews some of the recent literature that has assessed the extent to which nanomedicine has lived up to its promise. The chapter focuses on the notion of the transversality of nanomedicine and its molecular basis. It shows how across the three most important areas of contemporary biomedicine, such as predictive, personalized, and regenerative medicine, nanomedicine has crosscutting effects. In addition, the chapter highlights that despite its indisputable novelty, the nanomedicine paradigm in some areas, particularly predictive and personalized medicine, builds on and intensifies already-existing tendencies within biomedicine. Finally, It recapitulates a paradox associated with each of the three areas and argues that these paradoxes provide useful sites for monitoring and reflecting on the development of nanomedicine.
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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 | 0.011 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.028 |
| Scholarly communication | 0.013 | 0.019 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.004 | 0.010 |
| Insufficient payload (model declined to judge) | 0.006 | 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".