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
All major pharmaceutical companies are currently investing significantly in the development of medicines with a nanotechnology component. Such research promises therapeutic drugs with greater efficacy and a wider range of clinical indications. Nanomedicines are just beginning to enter drug regulatory processes, but within a few decades could comprise a dominant group within the class of innovative pharmaceuticals. The current thinking of government safety and cost-effectiveness regulators appears to be that these products give rise to few if any nano-specific issues. This article challenges that proposition and seeks to explore what features of nanomedicines may create unique or heightened policy challenges for government systems of cost-effectiveness regulation. The Australian Pharmaceutical Benefits Scheme (PBS) is a key exemplar of the latter type of regulation in that it links expert scientific evaluation of cost-effectiveness with the pricing of PBS-listed drugs. In the current global financial crisis such systems are likely to become increasingly attractive and how they handle the demands made upon them by nanomedicines (including by application of a variation of the precautionary principle) is likely to be of considerable interest to policy makers worldwide.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it