Bibliographic record
Abstract
Globally, 177 nations are party to the Ramsar Convention on Wetlands of International Importance. This environmental treaty is unique as the only multilateral environmental agreement to actively conserve sites on the ground. The Ramsar Convention facilitates global scientific cooperation, among the largest and smallest nations, including Russia, Canada, and China, and Monaco and the Marshall Islands. This article presents a new global dataset to understand whether domestic political parties in the USA affect the US implementation of the Ramsar Convention. In total, this analysis stretches from 2005 to 2020, and uses the environmental conventions index (ECI). The ECI is the first cross-comparable and empirical dataset on the implementation of the Ramsar Convention and can jumpstart additional research. The implementation evidence suggests that the translation of Ramsar Convention policy into action in the USA remains consistent across political party. The highest environmental implementation scores occurred under a US Republican administration. This finding is surprising because Pew surveys have shown that Republicans care less about climate change than do Democrats. This perspective aims to generate re-thinking of US politics at the start of an incoming Republican administration. With bipartisan support, the Ramsar Convention could revive US environmental leadership.
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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 | 0.003 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".