Disease and prejudice: risk attribution to ethno-racial groups over the course of a pandemic
Bibliographic record
Abstract
Past research suggests that disease outbreaks drive prejudice towards minorities as they increase economic and disease threats. Based on an open-ended survey question distributed to 7,902 German residents over the course of one year of the Covid-19 pandemic (April 2020 to April 2021), we investigate the link between life-threatening events and ethno-racial prejudice. We find that pandemic-related threats only drive respondents’ tendency to scapegoat ethno-racial groups if they hold left and center leaning ideologies. However, for far-right supporters who are the most likely to attribute the spread of Covid-19 to ethno-racial groups, pandemic-related threats do not affect that attribution. We further find that threat theories are of limited relevance for explaining which ethno-racial groups are targeted: respondents held Chinese accountable at the beginning of the pandemic but quickly shifted their attention to immigrants – a salient figure in pre-Covid-19 rightist rhetoric. We show that ideology, more than pandemic-induced threat, continues to drive prejudice and demonstrate the under-utilized advantages of using open-ended survey questions for understanding the dynamics of intergroup prejudice.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".