Navigating diverse human–nature worldviews for more inclusive conservation
Bibliographic record
Abstract
Different worldviews shape how humans perceive, understand, inhabit, and value the world. Major efforts to achieve more inclusive conservation, such as the Kunming-Montreal Global Biodiversity Framework, seek to more fully reflect diverse worldviews in science, policy, and practice. Building on the Intergovernmental Platform on Biodiversity and Ecosystem Services Values Assessment's comprehensive review of academic publications, Indigenous and local knowledge sources, and policy documents, we characterize 4 human-nature worldviews: anthropocentrism, biocentrism, ecocentrism, and pluricentrism. This heuristic typology can help conservation scholars and practitioners navigate participatory decision-making by providing conceptual clarity to distinguish particular worldviews and the fuzzy boundaries between them, and by addressing practical issues, particularly discursive and structural power dynamics, that affect worldview expression. Two case studies, protected area prioritization in India and payments for ecosystem services in Colombia, show that inclusive conservation depends on strategies and abilities to recognize and understand diverse worldviews and to articulate them in institutions. These examples highlight that engaging diverse human-nature worldviews applies not only to developing new policies but also to adapting mainstream instruments.
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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 | 0.047 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.012 | 0.074 |
| Scholarly communication | 0.022 | 0.027 |
| Open science | 0.002 | 0.017 |
| Research integrity | 0.003 | 0.007 |
| 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".