Bear Creek Mining Corporation v Republic of Peru:1Two Sides of a ‘Social License’ to Operate
Bibliographic record
Abstract
The idea that private actors should hold a ‘social license’ to operate relies on expectations from community members surrounding the operations of a corporate actor and the integration of those expectations in business practices over time.4 While the concept is widely used in the literature on corporate social responsibility (CSR), it is often considered, at most, as ‘soft law’ that fails to rise to the realm of legal obligation.5 Given that the term ‘social license’ does not appear to be used in the text of international investment agreements (IIAs),6 the fact that it has not been discussed by tribunals is not surprising. With the exception of a brief reference to the concept in Copper Mesa v Ecuador,7 the idea of a social license to operate had not appeared in any publicly available IIA arbitral decisions until Bear Creek v Peru. The discussion of ‘social license’ in the Bear Creek v Peru Award is both novel and significant. An important part of the Tribunal’s reasoning is premised on the assumption that, in light of relevant international instruments, consultations with indigenous communities must be held with a view to obtaining consent from all relevant communities impacted by an investment project. While the express consideration of this social license did not prevent the Tribunal from finding that the measures adopted by Peru violated the provisions of the Free Trade Agreement between Canada and the Republic of Peru (Canada–Peru FTA),8 the Award sheds light on two diverging conceptions of a social license to operate. On the one hand, the majority of the Tribunal emphasized an obligation of the State to monitor closely the efforts conducted by the investor to obtain consent from indigenous communities and to voice its concerns throughout the consultation process. On the other hand, the Partial Dissenting Opinion of Professor Philippe Sands suggests that obtaining a social license is the responsibility of the investor and that failure to secure this license should have been taken into consideration by the Tribunal. These diverging views evidence the two sides of a ‘social license’ to operate: the foreign investor’s obligation to obtain a ‘social license’, and the State’s role in monitoring the process by which that consultation and consent occur.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.009 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.009 | 0.010 |
| Insufficient payload (model declined to judge) | 0.014 | 0.001 |
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".