Risky Business or Risky Politics: What Explains Investor-State Disputes?
Bibliographic record
Abstract
Although not a clear cut question of treaty compliance, this project takes as its theoretical point of departure two potentially opposing explanations for state compliance with international agreements, and asks whether investor-state disputes are better explained by shifting state preferences toward FDI (or a particular investment), or the lack of state capacity to maintain an investment-friendly environment. The project is structured around three sub-research questions: 1) which domestic institutions are taking the measures that are subsequently challenged by investors? What is the content of these measures? Against investors in which industries are these measures being taken? 2) Under what economic and political conditions are investor-state arbitration cases most likely to occur?3) Are these changes in policy toward investment the outcome of a shift in preferences on the part of state actors toward investment, or are they instead the result of a lack of institutional capacity to respect IIAs? This project adopts a mixed-methods approach to the research question, with empirical chapters based on the qualitative coding of an original dataset of investor-state disputes; a regression analysis, and three case studies of specific disputes in Canada, El Salvador, and Hungary. Therefore, this project paints a general picture of investor-state disputes not as the result of a failure of bureaucratic capacity, but as incidences in which (private) transnational actor preferences truly conflict with those of domestic actors, and in which the state chooses its obligation to the latter rather than the former. If we accept that investor-state arbitration has the potential to impose significant costs on states, it is important when either justifying or criticising the regime to have an understanding of for which policy measures, and at whose behest, states are incurring these costs. These findings in turn have relevance for those who wish to improve investor-state relations and avoid investor-state disputes, as well as attempts to reform the investment arbitration system.
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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 | 0.009 | 0.047 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.003 | 0.016 |
| Scholarly communication | 0.008 | 0.012 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".