The Partnership Prescription: Access to HIV/AIDS-related Medicines and Public—Private Partnerships
Bibliographic record
Abstract
Globalizing forces, including increasing interconnectivity in trade, finance, technology, communications, and population mobility, have created impacts and challenges for public health that transcend national boundaries. Neither communicable nor noncommunicable diseases can be contained or addressed solely within individual states. Furthermore, poverty-related diseases (HIV/AIDS, tuberculosis, malaria, etc.), have reached epidemic proportions, and have cross-cutting and complex economic, social, and political determinants and impacts. There is a growing awareness that ‘institutions matter’ in devising responses to complex global health issues (Dodgson et al., 2002; Kickbusch, 1997). One of these, enthusiastically recommended as a model for overcoming existing institutional deficiencies, is the creation of global public-private partnerships in health (GPPPs) (Reinecke et al., 2000). A GPPP is a collaborative relationship formed between at least three parties: (1) a corporation or industry association; (2) intergovernmental organizations; and (3) national authorities (Buse and Walt, 2000). Global public-private partnerships are created to develop new products (that is, drugs and vaccines), improve access to products, assist with global coordination mechanisms, strengthen health care services, provide public advocacy and education and for regulatory and quality assurance purposes (Nishtar, 2004). These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.011 | 0.012 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.010 | 0.007 |
| Insufficient payload (model declined to judge) | 0.058 | 0.006 |
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".