Replication Data for: Inclusive Refugee-Hosting Can Improve Local Development and Prevent Public Backlash
Bibliographic record
Abstract
Large arrivals of refugees raise concerns about potential tensions with host communities, particularly if refugees are viewed as an out-group competing for limited material resources and crowding out public services. To address these concerns, calls have increased to allocate humanitarian aid in ways that also benefit host communities. This study tests whether the increased presence of refugees, when coupled with humanitarian aid, improves public service delivery for host communities and dampens potential social conflict. We study this question in Uganda, one of the largest and most inclusive refugee-hosting countries. The data combines geospatial information on refugee settlements with original longitudinal data on primary and secondary schools, road density, health clinics, and health utilization. We report two key findings. First, even after the 2014 arrival of over 1 million South Sudanese refugees, host communities with greater refugee presence experienced substantial improvements in local development. Second, using public opinion data, we find no evidence that refugee presence has been associated with more negative (or positive) attitudes towards migrants or migration policy.
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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 | 0.010 | 0.067 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.072 | 0.037 |
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".