Polenta and Cyanide? Investment Arbitration as Prospective Environmental Injustice in Roșia Montană
Bibliographic record
Abstract
Around the world, local communities supported by national and transnational advocacy networks are fighting to defend or preserve their homes and livelihoods from extractivist projects that threaten their environments. In this chapter, we look at Investor-State Dispute Settlement (ISDS) as a form of prospective environmental (in)justice (PEJ). ISDS provides for multinational corporations to sue states when they have a grievance over the state’s treatment of their investment. We argue that ISDS continues the structural violence of extractive projects and the pre-project harms resulting from foreign investor-welcoming climates. The chapter draws on empirical research on the Roșia Montană case in Romania to extend the theory of PEJ to scenarios where communities have succeeded in stopping a mining project, but the investor brings arbitration against the state, thus prolonging the “soft” extractive violence. We analyse how grassroots movements formed coalitions with national and foreign NGOs, succeeded in stopping a Canadian mining project based on cyanide extraction, and inscribed Roșia Montană as a UNESCO World Heritage site. In response, the Canadian mining company instigated investment arbitration proceedings against Romania. The case illustrates that, despite the legal victory of the Romanian state, international investment arbitration potentially allows “green crime”, rendering it awfully lawful.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.011 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".