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Record W4406037096 · doi:10.1093/arbint/aiae044

The Colombian Santurbán Páramo saga and its contribution to the development of international investment law and arbitration

2025· article· en· W4406037096 on OpenAlexaboutno aff
Nicolò Andreotti

Bibliographic record

VenueArbitration International · 2025
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicInternational Arbitration and Investment Law
Canadian institutionsnot available
Fundersnot available
KeywordsExpropriationTreatyArbitrationAdjudicationInvestment (military)LawInternational lawInternational investmentPolitical scienceForeign direct investmentBusinessEconomyInternational tradeEconomics

Abstract

fetched live from OpenAlex

Abstract The Colombian Santurbán Páramo saga is a series of three arbitral proceedings initiated by Canadian companies claiming that Colombia’s actions with regard to the protection of the Santurbán Páramo area, a high-altitude wetland ecosystem on the Colombian Andes, had negatively impacted their mining activities. The three proceedings, which concluded with three awards in 2024, were characterized by the presence of different interests at stake. In fact, while markets represent the Santurbán Páramo as a source of gold to feed the global economy, local communities see the area as a key source of water as well as a fragile ecosystem deserving protection. From an international investment law’s standpoint, the analysis of the Colombian Santurbán Páramo saga offers the possibility to reflect on several key aspects of investment treaty provisions as well as customary international law. In this light, the findings of the three tribunals on expropriation, minimum standard of treatment, and exception clauses will likely be considered by future investment tribunals which have to adjudicate environmental-related disputes. This holds a fortiori true if one considers that more and more disputes will be based on new-generation investment agreements, which generally contain provisions like those of the Canada-Colombia Free Trade Area.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.966
Threshold uncertainty score0.561

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.013
GPT teacher head0.244
Teacher spread0.231 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2025
Admission routes1
Has abstractyes

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