Adjudicating Trade and Investment Disputes
Bibliographic record
Abstract
Recent trends suggest that international economic law may be witnessing a renaissance of convergence – both parallel and intersectional. The adjudicative process also reveals signs of convergence. These diverse claims of convergence are of legal, empirical and normative interest. Yet, convergence discourse also warrants scepticism. This volume contributes to both the general debate on the fragmentation of international law and the narrower discourse concerning the interplay between international trade and investment, focusing on dispute settlement. It moves beyond broad observations or singular case studies to provide an informed and wide-reaching assessment by investigating multiple standards, processes, mechanisms and behaviours. Methodologically, a normative stance is largely eschewed in favour of a range of 'doctrinal,' quantitative and qualitative methods that are used to address the research questions. Furthermore, in determining the extent of convergence or divergence, it is important to recognize that there is no bright line or clear yardstick for determining its nature or degree.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.018 | 0.042 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.010 | 0.024 |
| Scholarly communication | 0.017 | 0.012 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.006 | 0.006 |
| 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".