Replication Data for: Poverty and Prejudice: Evidence from Myanmar
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Does poverty drive prejudice? We study this question in Myanmar, a deeply divided society where anti-Muslim sentiment surged during a partial democratic transition in the mid-2010s. Drawing on theories of economic competition and scapegoating, we test whether material hardship predicts exclusionary attitudes using new data from a nationally representative survey of 22,000 adults belonging to the majority Buddhist group. We find a large and consistent association: both poorer individuals and poorer townships are more likely to express Islamophobia. This relationship persists when leveraging a plausibly exogenous income shock caused by severe flooding. Poverty is more predictive of anti-Muslim sentiment than key alternative explanations for intergroup animus. It also correlates with hostility toward other minorities (Hindus and Indians), indicating that poverty is tied to a more general tendency to denigrate outgroups. Our findings shed light on the economic foundations of polarized social preferences and may help identify communities at heightened risk of ethnoreligious conflict.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.044 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.009 |
| Open science | 0.014 | 0.024 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.018 | 0.117 |
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 it