Main Act or Side Show? Model Agreements by International Institutions and Their Reuse in Investment Treaty Texts
Bibliographic record
Abstract
ABSTRACT Scholars and negotiators often assert that model treaty texts published by international institutions (IIs) shape investment treaty design. This paper empirically investigates the reuse of international institutions’ treaty templates. It tracks the imprint of six international institution templates on the text of negotiated international investment agreements (IIAs) using the Electronic Database of Investment Treaties. We find that the overall impact of international institution models has been low. No international investment agreement in our dataset was copied from an international institution’s model wholesale. On average, annual similarity between model texts and negotiated investment treaties is lower than 40% and significantly lower than the influence of international institutions’ models in the structurally similar international tax treaty regime. However, we do find evidence of an impact of international institutions' language on specific salient clauses. For example, the text of key investment protection clauses in the 1967 Draft Convention of the Organization of Economic Cooperation and Development was reproduced in hundreds of international investment agreements and novel clauses on investor responsibility first introduced in the 2006 International Institute for Sustainable Development model have subsequently been copied verbatim into negotiated international investment agreements. Our work concludes by discussing explanations for the comparatively low imprint of international institutions, notes other pathways for these institutions to influence treaty design, and sketches out an agenda for future research.
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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 | 0.012 | 0.153 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.008 | 0.013 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".