Internal Migration and Resource Conflict: Evidence from Riau, 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
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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