Penal extractivism: A qualitative study on punishment and extractive industries in Peru
Bibliographic record
Abstract
This article introduces the concept of penal extractivism in the punishment and society literature. We define penal extractivism as the punitive strategies that a state implements to safeguard extractive industries from citizens’ contention. This concept addresses the limitations of categories like criminalization, protest policing, social control, and labour discipline while bridging the gap between punishment studies and research on extractive industries. Additionally, we draw upon evidence of the Espinar mining conflict in Peru to explain five punitive strategies the state uses to handle protests: (1) off-duty policing and critical assets legislation, (2) state of emergency declarations, (3) police or prosecutorial notes against environmental defenders, (4) criminal indictments, and (5) the transferring of criminal cases to distant jurisdictions. Based on our findings, we argue that penal extractivism is a dynamic and ambivalent project that targets marginalized rural populations. The state partially deters mobilizations but fails to address the underlying social unrest, reinforcing the conditions that perpetuate mining conflicts. This in-depth within-case analysis examines the relationship between punishment and extractivism in the global context of contemporary social mobilizations.
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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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.009 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| 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".