Structural Conditions for Novelty: The Introduction of New Environmental Clauses to the Trade Regime Complex
Bibliographic record
Abstract
When do parties introduce novel clauses to a system of contracts or treaties? While important research has investigated how clauses diffuse once introduced, few empirical studies address their initial introduction. Drawing on network theory, this paper argues that novel clauses are introduced when agreements are concluded in certain structures of earlier agreements and the clauses they include. This paper demonstrates this argument using the example of 282 different environmental clauses introduced into the trade regime complex through 630 trade agreements concluded between 1945 and 2016. We find that trade agreements are more likely to introduce novelties when they involve parties with a diversity of experience with prior environmental clauses, and introduce more novelties when more parties are less constrained by prior trade agreements between them. Contrary to prevailing wisdom, power asymmetry between the negotiating parties is not statistically significant.
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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 | 0.011 | 0.048 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.027 |
| Scholarly communication | 0.009 | 0.017 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.012 | 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".