Critical mineral (in)securities: Techno-legal fixes and the reproduction of socio-environmental abuses
Bibliographic record
Abstract
In response to China’s dominance, the European Union and the United States securitize critical mineral supplies. They also aim to address potential environmental and socio-economic challenges associated with increased industrial activities. Building on political ecology and critical geography, we examine the legal and technical measures—referred to as techno-legal fixes—designed to balance the need for secure mineral supplies with socio-environmental protection. We argue that the EU’s Critical Raw Materials Act and Battery Passport, alongside the U.S.’ Inflation Reduction Act and Justice40 Initiative, serve as capitalist trade-offs that perpetuate harmful practices justified by low-carbon transitions. Techno-legal fixes reflect a securitization framework integrating socio-environmental costs with the goal of accelerating green extractivism, rather than pursuing more radical alternatives that would address the contradictions of “green growth.” We highlight the impact of European policies on the Democratic Republic of the Congo, where critical minerals are extracted. We also explore the implications of U.S. efforts to revitalize its domestic mining sector. By exposing the abuses and inequities embedded within these techno-legal fixes as well as the rise of neo-illiberalism and shift from “green” to “naked” extractivism, we point at the limits of a conflicting governance system characterizing the challenges of the transition to a low-carbon economy.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.030 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".