The International Regimes Database: Designing and Using a Sophisticated Tool for Institutional Analysis
Bibliographic record
Abstract
This article presents the International Regimes Database (IRD), an analytical tool designed to (i) move regime analysis from its current emphasis on the use of discrete case studies to the use of a relational database encompassing comparable data on a large number of cases and (ii) facilitate quantitative as well as qualitative analyses of hypotheses dealing with international regimes. The article describes the architecture of this database, introduces some preliminary findings relating to compliance, decision rules, and programmatic activities, and discusses methodological issues pertaining to the use of the database on the part of other scholars. It provides a short and easily accessible introduction to the book-length treatment of the IRD contained in: Helmut Breitmeier, Oran R. Young, and Michael Zürn, Analyzing International Environmental Regimes, Cambridge, MA: MIT Press, 2006.
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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 | 0.025 | 0.070 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.016 | 0.020 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.015 | 0.019 |
| Open science | 0.005 | 0.011 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.020 | 0.012 |
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".