Bibliographic record
Abstract
In a world of growing public interest in global matters and criticisms of multilateralism to adequately address them, the role of international courts and tribunals in the resolution of disputes is shifting. A central aspect of this shift is whether and how international courts and tribunals can be used to resolve such disputes in the public interest. This practice, referred to as public interest litigation, is the object of this collection, which identifies some recent developments, trends and prospects in this growing practice. Its aim is to assess the degree to which the bilateral design of international courts and tribunals can adapt to the shift towards a public approach to international litigation. Engaging with various fields where public interest litigation exists – such as human rights, climate change, global health and criminal law – it identifies recent developments, trends and prospects in this practice. The selected pieces provide a flavour of the types of issues that have arisen before international judicial bodies – for instance, the International Court of Justice, the International Tribunal for the Law of the Sea, international arbitral tribunals, regional human rights bodies or criminal courts – and explores issues that may arise in the future.
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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.006 | 0.012 |
| Scholarly communication | 0.015 | 0.012 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.009 | 0.008 |
| Insufficient payload (model declined to judge) | 0.019 | 0.004 |
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".