Bibliographic record
Abstract
Some organisations make vaccination a condition of employment. This means prospective employees must demonstrate they have been vaccinated (eg, against measles) to be hired. But it also means organisations must decide whetherexistingemployees should be expected to meet newly introduced vaccination conditions (eg, against COVID-19). Unlike prospective employees who will not behiredif they do not meet vaccination conditions, existing employees who fail to meet new vaccination conditions risk beingfired. The latter seems worse than the former. Hence, objections to vaccination mandates commonly centre on the harms that will be visited on existing employees who are unwilling to be vaccinated. However, because this objection does not necessarily entail the claim that vaccination is unnecessary for the effective and safe performance of certain jobs, those making this objection should have less of an objection, or no objection at all (at least on these grounds), to introducing vaccination requirements in some cases forprospectiveemployees. Yet, in this paper, I shall argue that if one has reason to believe vaccination requirements can be justified for prospective employees, one should also believe they are justified for existing employeesdespiteany asymmetry in consequences experienced by the two groups. As a consequence, common objections made against vaccination mandates grounded solely in the harms that may be experienced by existing employees who are unwilling to be vaccinated should be considered unpersuasive.
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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 | 0.021 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.005 | 0.030 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.008 | 0.006 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".