Empirical Evidence for the Emergence of Global General Normative Force in the Paris Agreement
Bibliographic record
Abstract
The Paris Agreement presents a unique framework to address the pressing dangers of climate change. Given the magnitude of this global problem and our reliance on international treaties to counteract its adverse effects, it is imperative that we understand the evolving norms within this domain. To evaluate the extent to which the Paris Agreement serves as a second-order "rule of recognition" in the context of global climate governance, we utilize a straightforward test rooted in Hart's theory of the rule of law. Our approach entails enumerating the number of countries worldwide that have enacted laws explicitly referencing the Paris Agreement, and in order to classify the agreement as achieving normative force, we verify that each of these references portrays the Agreement as promoting the existence of the law in question. To test whether this denotes a shift in global climate governance, we employ the same measure for the Montreal and Kyoto Protocols as a counterfactual. Our findings indicate that at least one legally binding document in 149 countries, which accounts for 92% of the global population, references the Paris Agreement. The advent of this nearly universal, law-like institutional structure underscores the Agreement's growing normative force, signifying the establishment of a new regime in global climate governance.
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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 | 0.023 | 0.088 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.003 | 0.012 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 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".