Vaccine mandates for prospective versus existing employees: reply to Smith
Bibliographic record
Abstract
Employment-based vaccine mandates have worse consequences for existing than prospective employees. Prospective employees are not yet dependent on a particular employment arrangement, so they are better positioned to respond to such mandates. Yet despite this asymmetry in consequences, Smith argues that if vaccine mandates are justified for prospective employees, they are similarly justified for existing employees. This paper responds to Smith's argument. First, Smith holds that bona fide occupational requirements are actions that are necessary for the safe and effective completion of one's job. As such, they apply to existing and prospective employees alike. However, I argue that the existence of effective alternative interventions precludes vaccination from being considered a bona fide occupational requirement under current circumstances. Second, Smith holds that if a requirement is justified for prospective employees, it is justified for existing employees, despite the asymmetry in consequences. However, I argue that since vaccination is not a bona fide requirement, the asymmetry in the harms of mandates experienced by prospective versus existing employees entails an asymmetry in the justification required to mandate vaccination for each group. As such, vaccination can be considered a requirement for prospective employees while not being required for existing employees.
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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 | 0.013 | 0.065 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.010 |
| Scholarly communication | 0.004 | 0.011 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.059 | 0.056 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".