Convergence and Divergence in the Investment Treaty Universe – Scoping the Potential for Multilateral Consolidation
Bibliographic record
Abstract
How far are we from a multilateral investment treaty? In this paper we answer this question by empirically assessing convergence and divergence in the pool of existing bilateral investment treaties (BITs) scoping the potential for multilateral consolidation. To do so, we introduce a novel automated coding procedure, which investigates investment treaty content across 1628 English-language BITs and their 22,500 articles. We show that treaties are split into older, short and shallow agreements and newer, deep and complex ones. This creates possibilities for consolidation around a lowest common denominator. A multilateral treaty with the 27 most prevalent features (out of a total of 66 coded features) would already substitute the content of 50% of all BITs and one with the 36 features could replace 80% of agreements. In contrast, consolidating practice around deeper agreements balancing investment protection and State sovereignty explicitly is politically more desirable, but also more ambitious. Only a minority of treaties contain non-investment protection features and their design diverges increasingly as States adopt varying architectures to solve similar policy challenges. As a result, further consolidation at the regional level and partial multilateralizations become necessary stepping-stones, if a future multilateral investment agreement is to converge practice around deeper BITs.
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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 | 0.093 | 0.335 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.023 | 0.026 |
| Science and technology studies | 0.004 | 0.012 |
| Scholarly communication | 0.016 | 0.027 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".