Pull me – push you? The disparate financing mechanisms of drug research in global health
Bibliographic record
Abstract
BACKGROUND: There is an inconsistency in the way pharmaceutical research is financed. While pull mechanisms are predominantly used to incentivize later-stage pharmaceutical research for products with demand in the Global North, so-called neglected diseases are chiefly financed by push funding. This discrepancy has so far been ignored in the academic debate, and any compelling explanation for why we draw the line between push and pull at poor people is lacking. MAIN BODY: Clinical development of new pharmaceuticals is chiefly financed by free market pull mechanisms. Even in cases where markets fail to deliver adequate incentives, demand enhancement mechanisms are used to replicate pull funding artificially, for example, with subscription models for antibiotics. Push funding in clinical research is almost always used when the poverty of patients means that markets fail to create sufficient demand. The general question of whether push or pull generally is the more efficient way to conduct pharmaceutical research arises. CONCLUSIONS: If the state is efficient in directing limited budgets for pharmaceutical research, push funding should be expanded to global diseases. If private industry is the more efficient actor, there would be enormous value in experimenting more aggressively with different approaches to enhance market demand artificially for neglected diseases.
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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 | 0.023 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.011 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 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".