Critical minerals or criminal miners? The (in)formalization of artisanal and small-scale copper mining in Peru
Bibliographic record
Abstract
• Peru’s artisanal and small-scale mining (ASM) regulatory framework distinguishes illegal, informal, and formal mining. • The (in)formalization process started in 2002 with deadlines extending over 20 years. • Informal ASM is conceptualized as a frontier moment: the critical mineral frontier is expanded through a temporary yet sustained informalization. • Informal copper miners are in a limbo between criticality and criminality: copper is legal enough, the miners are not fully legal. • Informalization has allowed Peru to postpone the most pressing problem in ASM formalization: the concession system. The formalization of artisanal and small-scale mining (ASM) has been at the center of international and national policy efforts aimed at addressing the problems in this sector. Peru has ASM in every region of the country and a mixed record of ASM formalization, with more success in in formalizing the miners and legalizing the commodities that enter the global market. Within the context of increased demand for copper in decarbonization efforts, this paper illustrates the expansion of the critical mineral frontier through informal copper ASM. I argue that the temporary yet unending in formalization policies of ASM constitute an institutionalized frontier moment , whereby informal miners are positioned in a limbo between criticality and criminality: their products enter the global supply chain as “legal-enough” critical minerals, but the miners remain not fully legal, effectively delinking resource users from the costs of extraction. To illustrate this, this paper analyzes Peru’s ASM formalization policies to showcase how the state has structurally created ASM informality. Then, empirically grounded on a “following the thing” methodology of informal mining in northern Peru, the paper highlights the implications of this frontier moment on miners and their communities, including elite capture through financing systems and shadow ownership, and increased vulnerability to violence, theft, and extortion by illegal miners and criminal organizations. Lastly, this paper highlights the contradictions between securing critical minerals and criminalizing the miners who extract them.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".