Do vaccine mandates impair the voluntariness of informed consent?
Bibliographic record
Abstract
An ethical and legal obligation generally exists for informed consent to be obtained prior to the administration of medical interventions. This includes vaccinations. For an individual’s informed consent to be valid, it must be given voluntarily . Hence, when individuals are required to be vaccinated—for example, as a condition of employment—we might ask whether this impairs the voluntariness of their informed consent, thereby rendering it invalid. If this turns out to be the case, then this would count as a pro tanto reason to think vaccine mandates are unethical. Assuming vaccine mandates count as ‘coercive’, interrogating this question requires an account of consent under third-party coercion, since the pressure or coercion exerted by vaccine mandates is exerted by third parties, such as employers, rather than the recipients of consent. Accordingly, this paper draws on Maximilian Kiener’s Interpersonal Consenter–Consentee Justification account of the voluntariness of medical consent under third-party coercion to develop an explicit argument as to why vaccine mandates do not vitiate the voluntariness of informed consent. Vaccine mandates do not necessarily impair the voluntariness of informed consent because, in such cases, the consent-receiver neither contributes to a vaccine mandate’s coercive threat nor wrongs the consent-giver in any way. Consent is not obtained by coercion in such cases, even though it may be motivated by it. The mere presence of third-party coercion does not invalidate consent since third parties cannot directly negate the voluntariness of consent.
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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 | 0.071 | 0.204 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.029 |
| Scholarly communication | 0.007 | 0.021 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.014 | 0.011 |
| Insufficient payload (model declined to judge) | 0.007 | 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".