The making of rights of nature: nine patterns in a decade of empirical research on social-ecological drivers and actors
Bibliographic record
Abstract
Abstract Rights of Nature (RoN) cases, where nature is granted legal rights, are rapidly gaining traction across the globe. Although there are many individual and comparative case studies, the extent to which emerging RoN cases share specific patterns that can be observed in the processes leading to the adoption of RoN is yet to be understood. To fill this gap, we provide a systematic literature review of the decade of empirical research on the RoN. Building on the Institutional Analysis and Development framework, our review uses an in-depth analysis, with a special focus on eliciting evolving patterns across cases. The analysis demonstrated that the drivers of RoN processes are extraordinarily complex and case-specific, shaping our understanding of RoN itself. The identified nine patterns show that understanding RoN as a ‘revolutionary ecocentric movement’ is too simplistic, and it should rather be understood as a boundary object that connects place-based non-human and human well-being and relations, as well as formal and informal institutional conditions. The review revealed that themes such as justice, property rights, and personal engagement of powerful actors have been key in driving RoN cases worldwide. Likewise, contrary to the conventional perception, concern for the environment is not a common driver of RoN, and Indigenous or local communities cannot be universally and simply described as advocating actors for RoN, while undoubtedly it is often their interests that are most affected by RoN reforms. However, RoN-related rules tend to create space for questioning the purely instrumental and short-term perspectives towards nature and for redirecting the focus to relational and representational thinking. We found that disciplinary silos contributed to the historically veiled image of RoN due to insufficient engagement with interdisciplinary and decolonizing research methods. The revealed patterns can guide scholars, practitioners, and policymakers in rapidly growing cases worldwide to learn from the existing empirical knowledge. Our study is particularly valuable in times when multiple local-to-global and increasingly acute challenges around nature and biodiversity are putting pressure on societies to develop more ‘fundamental’ or ‘transformative’ approaches bridging science, policy, and practice and especially those that can better integrate diverse knowledge systems of Indigenous and local communities.
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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 | 0.020 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.020 |
| Science and technology studies | 0.002 | 0.012 |
| Scholarly communication | 0.010 | 0.015 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".