Incentivizing at‐risk production capacity building for COVID‐19 vaccines
Bibliographic record
Abstract
Our study analyzes capacity management for promising vaccine candidates before regulatory approval (i.e., at‐risk capacity building) in the presence of production outsourcing and different operational challenges: misaligned interests, possible ex post negotiations, asymmetric information between developers and manufacturers, and government involvement. We develop analytical models to compare two vaccine production modes: (1) the integrated mode (a single company determines the at‐risk capacity and produces in‐house) and (2) the outsourcing mode (a manufacturer determines the at‐risk capacity and a developer determines a funding level to share the capacity‐building cost). Our study reveals that outsourcing can achieve a higher at‐risk capacity only if it can achieve sufficient cost savings compared to the integrated mode. Our research also proves that both vaccine production modes tend to underinvest in the at‐risk capacity. Following this, we suggest measures to improve the at‐risk capacity building in both vaccine production modes. Our signaling game model reveals that a developer with high competence cannot always send credible signals of its true competence level to the manufacturer. Our incomplete contract model verifies that the relative performance of the two vaccine production modes is robust when ex post negotiation occurs under the outsourcing mode; however, the two parties may show incompatible preferences for the ex post negotiation. Our study also analyzes the optimal allocation of government financial support to development funding and capacity funding to incentivize at‐risk capacity building. We present comprehensive guidelines for the different stakeholders to collectively contribute to ramping up the at‐risk capacity of promising vaccines.
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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 | 0.006 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 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".