Replication data for "Social movements are transformative agents for biodiversity conservation"
Bibliographic record
Abstract
This dataset was created in order to document the analysis of the manuscrit "Social movements are transformative agents for biodiversity conservation". Civil society has long been a catalyst for social change by reshaping structures, influencing values, and challenging power dynamics; however, its role in driving transformative change for biodiversity remains underexplored. To address this gap, we analyze 2,801socio-environmental mobilizations documented in the Environmental Justice Atlas (EJAtlas). These mobilizations produce diverse outcomes that reveal distinct spatial, temporal, and sectoral patterns and proactively and reactively respond to environmental impacts across the globe. Notably, about 40% of these mobilizations occur within the top 30% of global priority lands for species conservation and their actions contribute to the achievement of key Kunming-Montreal Global Biodiversity Framework targets focused on ecosystem protection, restoration, sustainable use, and inclusive spatial planning. Yet, one-third of mobilizations face repression, criminalization, or violence—pressures that are even more common in high-priority conservation areas. Moreover, mobilizations facing repressive outcomes contest environmental threats relevant for the KMGBF targets more extensively than those with progressive outcomes, underscoring the risks faced by movements driving biodiversity protection in critical regions. To amplify the transformative potential of socio-environmental mobilizations, we emphasize the importance of recognizing, strengthening, and protecting them through coordinated action among diverse social actors. By fostering collaboration and targeted resource allocation, these efforts can empower socio-environmental mobilizations to catalyze meaningful and lasting change for biodiversity conservation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 | 0.091 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.010 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.115 | 0.049 |
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".