Insights from Global Environmental Governance
Bibliographic record
Abstract
The field of international relations (IR) is fragmented along several lines, some stirring more debate than others: theoretical divides have been discussed ad nauseam in the so-called “great debates”; the split between qualitative and quantitative methods remains a recurring theme of discussion; disciplinary walls continue to structure academia; and the differences between European and North American traditions have flowed into recent fashionable exchanges. Countless conferences and publications have documented these divides, often calling for new bridges across those lines (Hellmann 2003). Answering these calls, an increasing number of books and articles in IR develop middle-range theories, rely on mixed methods, borrow from several disciplines, and are coauthored by researchers from different countries. Yet, fewer studies have addressed the mutual ignorance of the different thematic areas of IR—supposedly united by a joint interest in international affairs—and explored potential avenues for bridging them. Global environmental governance (GEG) is one of these thematic islands of the IR archipelago. It has its own key journals (such as Global Environmental Politics), its inescapable classical references (such as Garrett Hardin's Tragedy of the Common), and its own research program (such as a persistent interest in regime theory). GEG scholars read, cite, criticize, and build on each other. However, they remain relatively insulated from the rest of the archipelago, and reciprocally, other subfields in IR pay relatively little attention to GEG (Dyer 2010).
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.014 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".