Introducing the International Treaty Ratification Votes Database
Bibliographic record
Abstract
Abstract This research note introduces the International Treaty Ratification Votes Database, which covers more than 6,000 votes on the ratification of international treaties in Canada, Finland, France, Germany, Italy, Slovakia, Spain, Turkey, the United Kingdom, and the United States between 1990 and 2019. In addition, the database presents data on the voting behavior of ninety parties in eight of these countries, resulting in more than 11,000 party observations. The research note presents the two datasets with their two units of analysis, the parliamentary and the party level, and describes the main variables, reaching from descriptive vote and cabinet data to issue areas, comparative party family classifications, and actual voting records. Furthermore, we suggest avenues for using the data for future research on the domestic politics of foreign policy: Our data can be used to study patterns in the politicization of international treaties and organizations, ratification delays, legislative–executive relations, the party politics of foreign policy making, and the crisis of the liberal international order.
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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 | 0.006 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.016 | 0.021 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 0.015 |
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".