Fractured Extraction: Mining Firms, Provinces and Municipalities in the Decentralized Politics of China’s Rare Earth Production
Bibliographic record
Abstract
Abstract Amid intensifying geopolitical competition and accelerating climate commitments, China’s rare earth elements (REE) sector has emerged as a strategic asset and a site of political contestation. While existing accounts emphasize China’s dominance through central control, this article develops the concept of “fractured extraction” to show how REE governance is mediated by uneven, multi-scalar negotiations among central authorities, provincial governments, municipal actors and firms. Drawing on historical analysis and provincial case studies from Inner Mongolia, Jiangxi and Sichuan, we argue that China’s REE governance is marked by cycles of alignment and divergence, where central mandates around environmental reform, industrial upgrading and resource consolidation are selectively implemented, reinterpreted or resisted by subnational actors pursuing local development goals. This dynamic reflects not fragmentation or coherence but fracture : a provisional, relational mode of governance that persists across China’s evolving extractive landscape. We identify four interrelated processes – innovation, upgrading, financialization and formalization – through which fractured extraction materializes to develop a framework for understanding the politics of green industrialization and strategic resource governance that foregrounds subnational actors and the contested nature of China’s low-carbon transition.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| 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".