What We Know (and Could Know) About International Environmental Agreements
Bibliographic record
Abstract
Initiated in 2002, the International Environmental Agreements Data Base (IEADB) catalogs the texts, memberships, and design features of over 3,000 multilateral and bilateral environmental agreements. Using IEADB data, we create a comprehensive review of the evolution of international environmental law, including how the number, subjects, and state memberships in IEAs have changed over time. By providing IEA texts, the IEADB helps scholars identify and systematically code IEA design features. We review scholarship derived from the IEADB on international environmental governance, including insights into IEA membership, formation, and design as well as the deeper structure of international environmental law. We note the IEADB’s value as a teaching tool to promote undergraduate and graduate teaching and research. The IEADB’s structure and content opens up both broad research realms and specific research questions, and facilitates the ability of scholars to use the IEADB to answer those questions of greatest interest to them.
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 | 0.007 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.004 | 0.013 |
| Scholarly communication | 0.010 | 0.034 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.011 | 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".