Legal mobilization in a global context: the transnational practices and diffusion of rights-based climate litigation
Bibliographic record
Abstract
Abstract Our article offers an in-depth account of the role of the transnational practices of collaboration, storytelling, and learning in the diffusion of rights-based climate litigation (RBCL). Drawing on semi-structured interviews, participant observation, and quantitative data, we trace how the performance of these practices by lawyers, litigants, communities, scholars, and NGOs have fostered and sustained the transnational generation, exchange, and flow of resources, relationships, narratives, and knowledge underlying the field of RBCL. We argue that all three practices have fostered the diffusion of RBCL by influencing the local determinants of legal mobilization through enabling, discursive, and relational pathways. Finally, we show that these practices have had structural effects that have shaped the ideas and identities of the practitioners in the field of RBCL. Over time, the discursive and relational dimensions of practices have given rise to and have been strengthened by the formation of multiple communities of practice. The emergence of distinct communities provides the possibility for deeper forms of socialization and acculturation among their members, but they also make conflict and competition between different communities more likely. Overall, our article emphasizes the importance of understanding legal mobilization for climate justice as a set of practices that are shaped by the transnational social-legal context in which they are performed.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.018 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".