Inducing Immunity?:Justifying Immunization Policies in Times of Vaccine Hesitancy
Bibliographic record
Abstract
Why immunization must be made mandatory in times of vaccine hesitancy, and how we can design and implement immunization policies in a practical, trustworthy, and democratic way.<br/><br/>We live in perilous times when a significant number of citizens are either defiantly antivaccination or hesitant to accept vaccinations for themselves or for their children. In Inducing Immunity?, legal philosopher Roland Pierik and bioethicist Marcel Verweij explore ways to regulate collective immunization in as democratic a manner as possible. Approaching the problem as a matter of a conflict between the responsibility of government to protect public health and the basic right to freedom of citizens, Pierik and Verweij argue that John Stuart Mill's harm principle—the idea that individuals should be free to act so long as their actions do not harm others—offers a strong basis for coercive immunization policies.<br/><br/>Covering childhood immunization policies, as well as vaccination programs aimed at adult citizens, the authors argue that a coercive immunization policy in any liberal democracy must first satisfy the principle of proportionality. This leads them to an in-depth exploration of the role of exemptions, the nature of coercion, and the contents of vaccination programs. In the final part of the book, the authors also discuss the importance and scope of freedom of speech, given how the current spread of misinformation has undermined confidence in vaccines.<br/><br/>Offering an in-depth analysis in bioethics and legal philosophy, Inducing Immunity? is a sensible and applicable guide for health professionals, policymakers, and academics alike on how we can—and must—do better with our immunization policies.<br/>
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 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 | 0.008 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.046 | 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".