Internal Migration and Resource Conflict: Evidence from Riau, Indonesia
Bibliographic record
Abstract
Abstract A vast body of literature suggests that resource exploitation is linked to armed conflict. However, the role of voluntary internal migration in resource conflict has been overlooked. Does internal migration interact with resource exploitation and contribute to violent conflict in resource-rich regions of multinational states? And if so, how? Using a comparative ethnography approach, I inductively developed a four-part theory based on in-depth ethnographic fieldwork in resource-rich Inner Mongolia, China, before evaluating my theory against empirical evidence from Riau province, Indonesia. In contrast to the current literature that either sidesteps the role of voluntary internal migrants in resource conflict, or portrays them as mere negative externalities of resource exploitation, I show how migrants’ ownership of, and employment in, many of the companies that exploit and destroy local resources have marginalized local people and threatened their lifestyle and economic subsistence. As local elites resort to nativist frames to resist such practices and mobilize local people around these issues, companies hire brutal non-locally born, security guards or thugs to protect their assets, escalating the violence. Finally, states’ reliance on domestic population movements for resource exploitation and national development projects also affects their ability and willingness to intervene in resource conflict, contributing to their protracted nature. This article illustrates the problem with studying resource conflict in isolation from migration dynamics, as the two processes interact with one another, intensifying grievances and providing added motives and opportunities for violence.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".