Does knowledge matter? How a target's knowledge of their COVID‐19 infection during a violation of preventive policies affects perceived immorality and dehumanization
Bibliographic record
Abstract
Abstract During the COVID‐19 pandemic, behaviours that violated various precautionary policies were recurring. The present research examined how Chinese participants' perception of targets in terms of immorality and dehumanization depends on the target's knowledge of their COVID‐19 infection. In Study 1 (N = 223), we manipulated the presentation of the target's knowledge of their COVID‐19 infection before violating policies and observed that a target who knew they were infected was perceived as more immoral and less human than a target who knew they were not infected. In Study 2 (N = 267), we replicated this effect and further observed that a target was perceived as less moral and human even when they did not acquire knowledge of their COVID‐19 infection until after having violated the policies. Moreover, perceived immorality played a mediating role between the target's knowledge of their COVID‐19 infection and dehumanization, which was moderated by risk perception of COVID‐19 in Study 2, but not by fear of COVID‐19 in Study 1. These findings increase our understanding of the phenomenon of moralization in the context of a pandemic.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".