Mapping socio-institutional studies of biodiversity governance and climate change justice in wetland ecosystems
Bibliographic record
Abstract
Climate change is identified as a major threat to biodiversity, particularly wetlands, which cause or exacerbate injustice in societies. Overcoming injustice can support sustainable governance, including respecting and securing vulnerable groups’ rights and enhancing the ecological integrity of biologically diverse areas. A major challenge to achieving transformative governance is how actions to mitigate biodiversity loss or social injustice can lead to socio-ecological sustainability. Governance and climate change justice are complex, multidisciplinary, and multidimensional concepts that are increasingly used in disaster and climate change studies. This study examines the trend, similarities, differences, and linkage of biodiversity governance and climate change justice in wetlands using a bibliometric analysis in three periods of 1973–2000, 2001–2014, and 2015–2023, including reviewing their historical development, keywords, citation and co-citations, institutions, and country-wise. A rapidly growing number of publications regarding these concepts have been produced by different countries, particularly by institutions in developed countries. Governance and adaptation are connected to other concepts and are key concepts for integrated research on wetlands. Climate justice and governance can be bridging concepts in wetland ecosystem conservation. Developing a common understanding of climate change justice and biodiversity governance among relevant stakeholders is essential. Future studies should identify how to integrate climate change adaptation and mitigation with wetland 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 | 0.002 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.048 | 0.082 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| 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".