Establishing Government‐Operated Vaccine Programs: An Industry Perspective
Bibliographic record
Abstract
During 2000-2002, shortages of numerous routinely administered pediatric vaccines occurred. The reasons for these shortages were varied, but they included policy, manufacturing, and regulatory issues. The use of government manufacturing programs has been proposed as a way to stabilize the fragile vaccine supply and to prevent periodic shortages. Although such programs might be useful for defense needs, it is likely that such an approach would have limited value for routinely administered vaccines. Each of the vaccine components would require a dedicated manufacturing facility, and many components are administered in combination vaccines. Timing is also an important consideration. The restarting of an idled manufacturing facility would take many months; in addition, it often takes nearly 12 months to produce and release a single lot of vaccine. Finally, government-owned programs would face the same issues of regulatory changes, technological advancements, and facility updates as non-government-owned programs do--all of which would require sustained operation and investment. A secure and stable vaccine supply is best built by establishing the importance and value of our vaccine programs, which would, in turn, provide incentives to manufacturers to build capacity and inventories.
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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".