Ethnic Segregation and Public Goods: Evidence from Indonesia
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
This article contributes to the study of ethnic diversity and public goods provision by assessing the role of the spatial distribution of ethnic groups. Through a new theory that we call spatial interdependence , we argue that the segregation of ethnic groups can reduce or even neutralize the “diversity penalty” in public goods provision that results from ethnic fractionalization. This is because local segregation allows communities to use disparities in the level of public goods compared with other communities as leverage when advocating for more public goods for themselves, thereby ratcheting up the level of public goods across communities. We test this prediction on highly disaggregated data from Indonesia and find strong support that, controlling for ethnic fractionalization, segregated communities have higher levels of public goods. This has an important and underexplored implication: decentralization disadvantages integrated communities vis-à-vis their more segregated counterparts.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.010 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it