Ententes entre gouvernements et compagnies pharmaceutiques
Bibliographic record
Abstract
This report is part of a research project conducted by the University of Sherbrooke and the Center for Interuniversity Research and Analysis on Organizations (CIRANO). Its main purpose is to propose a framework agreement to govern the negotiation and conclusion of Product Listing Agreements (PLAs) between the Québec government and pharmaceutical companies. The objective of this report, which represents a stage of our research, is to better evaluate where the province of Québec stands with respect to this new trend of governments, public insurers, to enter into PLAs in order to reduce clinical and/or financial uncertainties linked to the reimbursement of new drugs. The report is not available yet> Le présent rapport s'inscrit dans le cadre d'un projet de recherche effectué par l'Université de Sherbrooke et le Centre interuniversitaire de recherche en analyse des organisations (CIRANO) dont l'objet est d'élaborer une proposition de contrat cadre permettant de régir la conclusion d'ententes entre le gouvernement du Québec et des compagnies pharmaceutiques. L'objectif de ce rapport, qui représente une étape de nos recherches, est de mieux situer le Québec par rapport à cette nouvelle tendance des gouvernements, assureurs publics, à conclure des ententes avec des compagnies pharmaceutiques dans le but de réduire les incertitudes, cliniques et/ou financières, associées au remboursement de nouveaux médicaments. Le rapport n'est disponible pour le moment>
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 | 0.017 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.008 | 0.007 |
| Scholarly communication | 0.010 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.019 | 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".