Ensuring Correctness or Promoting Consistency? Tracking Policy Priorities in Investment Arbitration through Large-Scale Citation Analysis
Bibliographic record
Abstract
This chapter investigates the relative importance investment tribunals accord to correctness and consistency considerations in arbitral decision-making by empirically investigating the similarity of treaties connected through precedent. My argument is that tribunals enjoy large discretion in their choice of precedent and use that discretion to further their legal policy preferences. Tribunals that take a system-oriented approach and understand their mandate as promoting the consistent and harmonious development of investment law will select precedent more liberally including cases rendered under highly dissimilar investment treaties. Conversely, tribunals that favor a dispute-centric approach and understand their mandate as ensuring the correct interpretation of the specific treaty will select precedent more cautiously focusing on cases rendered under the same or highly similar treaties, but excluding case law from dissimilar ones. Using a dataset of more than 4500 citations, I find that, apart from NAFTA and a few other exceptions, most tribunals cite precedent liberally rather than cautiously suggesting that tribunals prioritize consistency over correctness considerations. This conflicts with the preferences expressed by states in the UNCITRAL process that place correctness over consistency. Current investment law reform efforts are an opportunity to remedy this mismatch of policy preferences between states and tribunals.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Bibliometrics Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | high |
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 | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 itClassification
machine, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".