Rare earth mining in Kachin State, Myanmar:The impacts of rare earth mining on livelihoods and local communities
Bibliographic record
Abstract
The paper addresses the impacts of rare earth mining on livelihoods in Kachin State by focusing on two key dynamics. First, it examines how the expansion of mining over the past 10-15 years has affected agricultural livelihoods in areas where communities primarily depend on farming. Second, the paper explores the role that wage labour in the mining sector plays in local livelihoods, especially in light of the restrictions on cross-border labour migration to China following the outbreak of Covid-19 and amidst armed conflict, and the decline of agricultural livelihoods in mining regions. Addressing the social impacts of rare earth mining in Kachin State, this paper reveals the wide-ranging—and predominantly negative—consequences of the mining boom on local communities. It highlights six key issues: (1) sexual violence against women, (2) rising drug use and gambling, (3) community tensions over how to respond to mining, (4) the strengthening of armed actors’ control over local economies and communities, (5) relations between the Kachin Independence Army (KIA) and church and civil society organisations, and (6) rural-to-urban migration.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".