The New Terrain of Global Governance: Mapping Membership in Informal International Organizations
Bibliographic record
Abstract
We present a new dataset of membership in informal international organizations—IOs founded with non-binding instruments—which constitute one-third of operating IOs. We introduce state-IO-year–level membership data for 195 countries that complements the dataset on formal IOs from the Correlates of War Project. We explain our conceptualization of an informal IO, contrast it with other approaches, and detail the data collection process. We illustrate similarities and differences across formal and informal IOs, and across states and regions. We explain how our data validate or challenge conjectures about informal cooperation that have been inaccessible for lack of data. We demonstrate that while formal and informal IOs are similar in size, the composition of informal memberships in informal IOs is more fragmented. While informal IOs are a growing part of the governance portfolios of most states, some countries and regions participate more. We conclude by outlining elements of the research program our dataset unlocks.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".