Bibliographic record
Abstract
A subset of investor–State arbitrations arise out of circumstances where there is a major backlash by a local population against an investment, and the host State, in responding to such concerns, takes actions, such as cancelling a permit, which prevent the future operations of a foreign investor.3 Such cases raise fundamental questions about the legitimate concerns, rights and responsibilities of local populations affected by investment activities, host State governments and foreign investors.4 The Award in Bear Creek Mining v Peru,5 rendered under the investment chapter of the Canada–Peru Free Trade Agreement,6 is another such dispute. As outlined below, the case concerned Peru’s revocation of an authorization for the foreign investor’s acquisition of concessions for a silver mine, in response to widespread protests. The Award runs to some 300 pages, and this comment will focus on two of its contributions that are of wider relevance. First, the Tribunal considered the relevant standard for determining whether the investor had obtained a social license and, in particular, what was required of the investor when consulting indigenous populations affected by its operations, referring to the international law framework governing the latter issue. Second, the Award is one of the first to apply an investment treaty that included criteria intended to distinguish indirect expropriations from legitimate regulation and a general exceptions clause based on Article XX of the General Agreement on Tariffs and Trade (GATT).7 Both of these types of provisions have been included in many investment treaties over the last decade in an effort to increase States’ policy space, but they are largely untested before arbitral tribunals.8 This case comment will suggest that the Bear Creek Award highlights important ambiguities that remain in relation to such provisions and demand further attention from treaty drafters and arbitrators.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".