When municipalities get involved: Internal migration restrictions in Batam, Indonesia
Bibliographic record
Abstract
Abstract Over the last decades, subnational units have grown increasingly vocal in their desire to gain a say in immigration policymaking. Often overlooked, but increasingly prevalent, is the role played by subnational units in regulating internal migration into their respective territory, be it in attracting or more puzzlingly, in restricting internal migrants. Why do subnational units enact restrictive internal migration policies? Drawing from the existing literature examining local migration policies pertaining to international migration, as well as original empirical qualitative data collected during two rounds of fieldwork in Batam, Indonesia, this article proposes a novel Internal Migration+Institution, Geography and Elites (IM+IGE) framework, assessing the role of institutional and geographical factors as well as political elites in the context of large and rapid influx of internal migrants. The findings suggest that, while much can be learned from the literature on the local turn on immigration policymaking, the regulation of internal migration by subnational units remain sufficiently distinct to justify the development of a more fine-grained theory of local (restrictive) policy of internal migration, while also reiterating the need to better integrate lessons from the Global South.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".