International Law Association - Washington Conference (2014): Legal Principles Relating to Climate Change
Bibliographic record
Abstract
The International Law Association’s Committee on the Legal Principles Relating to Climate Change was established in 2008. The Committee has Professor Shinya Murase, Sophia University, as its Chair, Professor Lavanya Rajamani, Centre for Policy Research, as its Rapporteur, and over 30 of the world’s leading international environmental law academics as its members. The Committee, after six years of work, and three Reports, has completed its work. The Third (and final) Report of the Committee takes the form of Draft Articles titled ‘Legal Principles Relating to Climate Change’ and attached commentaries. The Third Report of the Committee was approved and adopted by the ILA Washington Conference in April 2014. These Draft Articles, based as they are on rigorous evidence-based research, and representing the views of leading academics and practitioners of international environmental law, have the potential to shape and influence the evolution of the climate change regime. They may also provide guidance on the design and implementation of the agreement that is slated to emerge from the UN Durban Platform negotiations due to conclude in 2015 and take effect from 2020.
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 | 0.017 | 0.026 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.013 | 0.006 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.022 | 0.017 |
| Insufficient payload (model declined to judge) | 0.019 | 0.011 |
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".