When Pandemic Threat Does Not Stoke Xenophobia: Evidence from a Panel Survey around COVID-19
Bibliographic record
Abstract
Studies have found that pandemics can heighten xenophobia among host citizens, often explained by the behavioral immune system theory (BIS) or elite-driven scapegoating. However, most research has overlooked the role of pandemic-related economic restrictions and job loss on sentiment toward immigrants. To isolate this economic mechanism, we examine the case of Venezuelan migrants in Colombia before and during COVID-19. Despite the Colombian government's severe economic lockdown, few politicians blamed Venezuelans for the pandemic. Thus, any economic impact on xenophobia should be evident. Using a panel experimental survey of 374 Colombians, supplemented with 550 new respondents at endline, we find no evidence that exposure to COVID-19 changed attitudes towards Venezuelans, even for those directly affected by the pandemic. Yet, those who did not lose their jobs viewed Venezuelan migration more positively at endline, providing support for the economic effects of pandemics.
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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 | 0.001 | 0.006 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 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".