Non-State Actors in International Law: Aims, Approach and Scope of Project and Legal Issues
Bibliographic record
Abstract
This is the first report of the newly established International Law Association (ILA) Committee on Non-State Actors (NSAs). It outlines the approach, aims and scope of its project of evaluating the challenges to international law posed by the proliferation of NSAs in the international political and legal arenas and maps the relevant legal issues. Following an introduction summarising the evolution of this Committee, this report has two parts: methodology and legal issues. Part one describes the aims, functional methodological approach to be employed and scope of the project and specifies working definitions and research questions. Part two maps rights and obligations, relating to NSA activities with respect to three principal functional categories of international law and global governance: (1) normcreation (treaty, customary, general principles and ‘soft’), (2) monitoring compliance (administration), and (3) enforcement (dispute settlement, accountability/responsibility and immunity). As it is the eventual aim of this Committee to derive NSAs’ legal ‘status’ or international legal personality (ILP) from empirical studies (for which the mapping exercise forms the basis), at the end of part two, the report considers the relevance and applicability of those concepts.
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 | 0.049 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.010 |
| Science and technology studies | 0.005 | 0.014 |
| Scholarly communication | 0.020 | 0.012 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".