Ethnic Mixing in Early Childhood: Evidence from a Randomized Field Experiment and a Structural Model
Bibliographic record
Abstract
We study the social integration of ethnic minority children in the context of an early childhood program conducted in Turkey aimed at preparing 5-year-old native and Syrian refugee children for primary school. We randomly assign children to groups with varying ethnic composition and find that exposure to children of the other ethnicity leads to an increase in the formation of interethnic friendships, especially for Turkish children. We also find that the Turkish language skills of Syrian children are better developed in classes with a larger presence of Turkish children. We then develop a model of friendship formation with two key mechanisms: preference bias and congestion in the friendship formation process. Structural estimation of the model suggests that interethnic exposure reduces the share of own-ethnicity friends (homophily) and has a non-monotonic effect on the propensity to form own-ethnicity friendships beyond what would be expected given the size of the group (inbreeding homophily). Counterfactual analysis indicates that improvement in the language skills of Syrian children can offset more than half of the effect that ethnic bias has on friendship formation patterns. Finally, we find that for Syrian children exposure to Turkish children in the pre-school program has a long-term effect on primary school absenteeism.
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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 | 0.046 | 0.066 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".