Perceptions of nature's contributions to people across an elevational gradient in eastern Nepal
Bibliographic record
Abstract
Abstract Equitable measures for nature conservation require an in‐depth understanding of human‐nature relations. Using qualitative and quantitative data from semi‐structured household surveys, we investigated people's perception of nature's contribution to their perceived well‐being along an elevational gradient in eastern Nepal. We used linear and ordinal regressions to identify the factors influencing these perceptions and qualitative analyses to identify nature's contributions to people (NCP) likely contributing to this well‐being. We found nuanced and context‐specific relationships between people and nature in Nepal, emphasising how geographic location, formal education, socio‐economic factors and gender shape perceptions of how nature contributes to well‐being. Participants provided examples of a variety of material, non‐material and regulating NCP that are crucial for multiple aspects of their well‐being, underscoring the need for integrated conservation approaches that extend beyond prioritising habitat maintenance to also encompass enhancing material and non‐material NCP. While conservation interventions may be informed by global conceptual frameworks and policy agreements such as the IPBES Conceptual Framework, the Kunming‐Montreal Global Biodiversity Framework and the Sustainable Development Agenda, they must be rooted in the collective perspectives and experiences of the local context in which conservation actually happens. Read the free Plain Language Summary for this article on the Journal blog.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".