The ‘Ethical’ COVID-19 Vaccine is the One that Preserves Lives: Religious and Moral Beliefs on the COVID-19 Vaccine
Bibliographic record
Abstract
Although the COVID-19 pandemic is a serious public health and economic emergency, and although effective vaccines are the best weapon we have against it, there are groups and individuals who oppose certain kinds of vaccines because of personal moral or religious reasons. The most widely discussed case has been that of certain religious groups that oppose research on COVID-19 vaccines that use cell lines linked to abortions and that object to receiving those vaccine because of their moral opposition to abortion. However, moral opposition to COVID-19 vaccine research can be based on other considerations, both secular and religious. We argue that religious or personal moral objections to vaccine research are unethical and irresponsible, and in an important sense often irrational. They are unethical because of the risk of causing serious harm to other people for no valid reason; irresponsible because they run counter to individual and collective responsibilities to contribute to important public health goals; and in the case of certain kinds of religious opposition, they might be irrational because they are internally inconsistent. All in all, our argument translates into the rather uncontroversial claim that we should prioritize people's lives over religious freedom in vaccine research and vaccination roll out.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.006 | 0.051 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.005 | 0.011 |
| Insufficient payload (model declined to judge) | 0.002 | 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".