Bibliographic record
Abstract
Global environmental governance is growing increasingly complex and recent scholarship and practice raise a number of questions about the continued feasibility of negotiating and implementing an ever-larger set of global environmental agreements. In the search for alternative conceptual models and normative orders, regional environmental governance (REG) is (re)emerging as a significant phenomenon in theory and practice. Although environmental cooperation has historically been more prevalent at the regional than at the global level, and has informed much of what we know today about international environmental cooperation, REG has been a neglected topic in the scholarly literature on international relations and international environmental politics. This introduction to the special issue situates theoretical arguments linked to REG in the broader literature, including the nature of regions, the location of regions in multilevel governance, and the normative arguments advanced for and against regional orders. It provides an overview of empirical work; offers quantitative evidence of REG's global distribution; advances a typology of REG for future research; and introduces the collection of research articles and commentaries through the lens of three themes: form and function, multilevel governance, and participation.
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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".