Constructing Rights of Nature Norms in the US, Ecuador, and New Zealand
Bibliographic record
Abstract
Governments around the world are adopting laws granting Nature rights. Despite expressing common meta-norms transmitted through transnational networks, rights of Nature (RoN) laws differ in how they answer key normative questions, including how to define rights-bearing Nature, what rights to recognize, and who, if anyone, should be responsible for protecting Nature. To explain this puzzle, we compare RoN laws in three of the first countries to adopt such laws: Ecuador, the US, and New Zealand. We present a framework for analyzing RoN laws along two conceptual axes (scope and strength), highlighting how they answer normative questions differently. The article then shows how these differences resulted from the unique conditions and processes of contestation out of which each law emerged. The article contributes to the literature on norm construction by showing how RoN meta-norms circulating globally are infused with differing content as they are put into practice in different contexts, setting the stage for international norm contestation.
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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 | 0.012 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.017 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".