Bear Creek V. Peru and the Legality of the Investment as a (Implied) Requirement for the Investment Arbitration Tribunal’s Exercise of Jurisdiction
Bibliographic record
Abstract
Concerns about inconsistency in the application of standards in arbitral awards are strongly present in investment treaty arbitration. In particular, tribunals can regularly exercise a varying scope of jurisdiction when they determine the legality requirement that demands foreign investments to be made in accordance with the law of the host state.In this paper, the author seeks to analyze the decision rendered by the tribunal in Bear Creek v. Peru, in which the Canadian mining company alleged that the Peruvian State breach, inter alia, expropriation protections under the Canada-Peru Free Trade Agreement in relation to its investment in the silver mining project of Santa Ana. In order to achieve this aim, in the first chapter, he addresses three key issues regarding the tribunal’s jurisdiction, the rights on which the company based its claim and the arguably prerequisite of legality or good faith for the tribunal’s exercise of jurisdiction. In the second chapter, he analyzes the validity of the tribunal’s interpretation on the legality requirement for investment as an implicit element in the relevant treaty to determine the tribunal’s jurisdiction.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.009 | 0.014 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.006 | 0.006 |
| 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".