Bibliographic record
Abstract
Nearly 10 per cent of the Gross Domestic Product (GDP) in France is spent on health care. Twenty per cent of this budget is spent on medicines, more than in many of the Organisation for Economic Co-operation and Development (OECD) countries including the US, UK and Canada. The pharmaceutical industry in France is the third largest in Europe and adopted product patents even before the TRIPS agreement. Strict regulatory measures govern the pharmaceutical industry in France. The branded drugs are costlier compared to the generics. In order to control costs and promote generic drugs in the prescription, the government has introduced several regulatory measures, which even other OECD countries have not fully implemented yet. Of the total turnover of the pharmaceutical industry, turnover from the domestic sales has been declining while the exports turnover has been increasing. The balance of trade in pharmaceuticals has been positive. The French have also been filing a large number of patents, second only to the US; and they rank higher than the US in patents granted. In order to compensate the firms for the loss of time in the patent application process, the French government grants a five-year term of exclusivity for companies satisfying certain criteria. Though this could delay the entry of generics, for pharmaceutical companies it provides an extended period of power over the product. The industry has also responded by investing in R&D to improve further. In conclusion, the government plays a significant role in providing health care and regulating the pharmaceutical industry.
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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.009 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 0.005 |
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".