Extractive Industries and Investor–State Arbitration: Enforcing Home Standards Abroad
Bibliographic record
Abstract
Extractive industries can bring much-needed jobs to remote locations in developing countries. At the same time, there are too many examples where extractive projects bring environmental degradation and human rights violations. Some research has pointed out that the difference between an extractive project that brings positive spillovers for communities and one that destroys communities and poisons the environment is a strong rule of law. However, developing countries often lack strong legal institutions and have high levels of corruption. Furthermore, remote locations are usually the last safe haven for vulnerable populations stricken by poverty. This essay argues that, given these circumstances, home states have a responsibility to do more to control their outward investors, particularly regarding extractive industries. The essay concentrates mostly on Canadian mining companies given the plurality of conflicts involving them and a developing country. The essay will also analyze current efforts at the home state level to rein-in the conduct of extractive industries abroad and advise where they may fall short.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.004 | 0.012 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.004 | 0.004 |
| 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".