Compliance politics and international investment disputes: a new dataset
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract The ability to ensure compliance with investor-state arbitral awards is often regarded as one of the strengths of the international investment regime. Yet, there have been few systematic studies of compliance to assess the extent to which states have actually complied with adverse investor-state compensation awards. This paper presents a new dataset that enables empirical research on compliance with these decisions; it is the first publicly available dataset to focus on what happens after awards are handed down, and in this way complements other databases on international investment law. This paper explains the data collection process (and its associated challenges), discusses the design choices made in selecting inputs and variables, presents a descriptive overview of the data, and examines how variables can be used in future research. Moreover, various cases are used as illustrations of the challenges of collecting and coding data on post-award processes and we explore what missing data can tell us about compliance dynamics.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it