A Tiered Vaccine Framework: Prioritizing Tier 1 Vaccines to Restore Public Confidence
Bibliographic record
Abstract
The term “vaccine” has been broadly and inconsistently applied to a range of products with widely divergent immunological outcomes, leading to the erosion of public trust and confusion among both medical professionals and the public. Historically, and by broad public understanding, a vaccine is expected to prevent infection, transmission, and disease through the induction of sterilizing, or true neutralizing immunity, specifically, the prevention of pathogen entry and replication in vivo. This ideal extends beyond the mere elicitation of neutralizing antibodies demonstrable in vitro. This paper proposes a three-tier classification system designed to differentiate products currently designated as “vaccines,” specifically to address the distinction between those that meet the traditional, highest-expectation definition (Tier 1), therapeutic vaccines that primarily prevent disease (Tier 2), and immunomodulatory therapeutics that primarily reduce disease severity (Tier 3). By detailing the mechanism of action of each product and emphasizing the urgent need for this refined classification, our aim is to restore public confidence in vaccination programs, improve understanding of vaccine-induced immunity among healthcare professionals, and empower informed decision-making by the public. We argue that a clearer understanding of vaccine capabilities will ultimately lead to increased vaccine uptake for those vaccines that do prevent infection, transmission, and disease.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.003 | 0.011 |
| Scholarly communication | 0.012 | 0.008 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 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".