Vaccines, Politics and Mandates: Can We See the Forest for the Trees? Comment on "Convergence on Coercion: Functional and Political Pressures as Drivers of Global Childhood Vaccine Mandates"
Bibliographic record
Abstract
Under-vaccination is a complex problem that is not simple to address whether this is for routine childhood immunization or for coronavirus disease 2019 (COVID-19) vaccination. Vaccination mandates has been one policy instrument used to try to increase vaccine uptake. While the concept may appear straight forward there is no standard approach. The decision to shift to a more coercive mandated program may be influenced by both functional and/or political needs. With mandates there may be patient and/or public push back. Anti-mandate protests and increased public polarization has been seen with COVID-19 vaccine mandates. This may negatively impact on vaccine acceptance ie, be counterproductive, causing more harm than overall good in the longer term. We need a better understanding of the political and functional needs that drive policy change towards mandates as well as cases studies of the shorter- and longer-term outcomes of mandates in both routine and pandemic settings.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".