Beyond the trees: Equity and justice in Nepal’s forest restoration
Bibliographic record
Abstract
We examined equity and justice in forest restoration programs in Madhesh province in Nepal. We selected four sites that have three different tenure regimes, namely, community forest (Baghbhairav and Musahar), collaborative forest management (Tamagadhi), and forest protection area (Dhanushadham). The primary data were collected through focus group discussion, key informant interviews, field observations, and forest cover change analysis. We used ArcGIS to perform a spatial overlay analysis to identify the forest cover dynamic (1990, 2000, 2010, and 2022), followed by an examination of equity and justice under its four dimensions: contextual, procedural, distributive, and recognition. Atlas.ti, a computer-assisted software, was used to create codes, sub-themes, and themes from qualitative information, which were then used for further interpretations. Our findings indicated that while forest cover in the study sites is changing rapidly, with temporal and spatial variation across the sites, restoration and equity are inextricably linked and mutually reinforce each other. We also identified visible interactions among the four dimensions of equity, with each contributing to restoration in a different way. The paper concludes that multiple factors undermine forest restoration, among them, equity and justice,community heterogeneity, weak tenure rights, and conflicts over forest benefits sharing are the prominent. The findings imply that, for successful restoration, equity and justice must be considered as the core elements for both intrinsic and instrumental reasons. • Forest cover in the Madhesh Province of Nepal is changing rapidly, with temporal and spatial variation. • Restoration and equity are inextricably linked and reinforce each other. • Interactions among the four dimensions of equity, and tenure arrangements are contributing to restoration differently . • Weak tenure rights, community heterogeneity and perceived inequity affected restoration outcomes. • Equity and justice are intrinsic and instrumental elements of restoration success.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.008 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 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".