Unity in Diversity? How Intergroup Contact Can Foster Nation Building
Bibliographic record
Abstract
Throughout history, many governments have introduced policies to unite diverse groups through a shared sense of national identity. However, intergroup relationships at the local level are often slow to develop and confounded by spatial sorting and segregation. We shed new light on the long-run process of nation building using one of history's largest resettlement programs. Between 1979 and 1988, the Transmigration program in Indonesia relocated two million voluntary migrants from the Inner Islands of Java and Bali to the Outer Islands, in an effort to integrate geographically segregated ethnic groups. Migrants could not choose their destinations, and the unprecedented scale of the program created hundreds of new communities with varying degrees of diversity. We exploit this policy-induced variation to identify how diversity shapes incentives to integrate more than a decade after resettlement. Using rich data on language use at home, marriage, and identity choices, we find stronger integration in diverse communities. To understand why changes in diversity did not lead to social anomie or conflict, we identify mechanisms that influence intergroup relationships, including residential segregation, cultural distance, and perceived economic and political competition from migrants. Overall, our findings contribute lessons for the design of resettlement policies and provide a unique lens into the intergenerational process of integration and nation building.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".