Kick‐starting diffusion: Explaining the varying frequency of preferential trade agreements’ environmental provisions by their initial conditions
Why this work is in the frame
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Bibliographic record
Abstract
Abstract Most recent preferential trade agreements ( PTA s) include environmental provisions. While a number of these environmental provisions remain rare and are incorporated in just a few PTA s, others are widely popular and are duplicated in more than 100 PTA s. We still lack a convincing explanation for this varying frequency. While the diffusion literature typically tries to explain how diffusion occurs, we investigate why certain provisions diffuse more often than others. We hypothesise that the initial conditions under which provisions first emerge determine the scope of their diffusion. Our results support this hypothesis and indicate that provisions originating from intercontinental agreements diffuse more often than others. At the same time, provisions first designed by economically powerful or environmentally credible countries are not related to more frequent occurrences of diffusion. These findings are of interest for the literatures on international institutions' design, interaction and diffusion.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it