Examining double standards in layoff preferences and expectations for gender, age, and ethnicity when violating the social norm of vaccination
Bibliographic record
Abstract
Whether vaccination refusal is perceived as a social norm violation that affects layoff decisions has not been tested. Also unknown is whether ascribed low-status groups are subject to double standards when they violate norms, experiencing stronger sanctions in layoff preferences and expectations, and whether work performance attenuates such sanctioning. Therefore, we study layoff preferences and expectations using a discrete choice experiment within a large representative online survey in Germany (N = 12,136). Respondents chose between two employee profiles, each with information about ascribed characteristics signaling different status groups (gender, age, and ethnicity), work performance (work quality and quantity, and social skills), and whether the employees refused to vaccinate against COVID-19. We found that employees who refused vaccination were more likely to be preferred and expected to be laid off. Respondents also expected double standards regarding layoffs due to vaccination refusal, hence, harsher treatment of females and older employees. Nonetheless, their preferences did not reflect such double standards. We found little support that high work performance attenuates these sanctions and double standards, opening questions about the conditions under which social biases arise. Our results suggest detrimental consequences of vaccination refusal for individuals, the labor market, and acceptance of health policies.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".