Justice-related impacts and social differentiation dynamics in Nepal's REDD+ projects
Bibliographic record
Abstract
Policies and projects aimed at Reducing Emissions from Deforestation and forest Degradation, and the sustainable management of forests and the enhancement of forest carbon stocks (REDD+), have been regarded as an opportunity to improve forest governance while supporting rural livelihoods. However, now that REDD+ policies are being increasingly implemented, a number of justice-related challenges have emerged, including how social heterogeneity should be approached to avoid deepening the unequal access to land, resources and livelihood opportunities or even violating human rights in rural contexts. Applying an environmental justice lens, this article analyses the experience of three local communities in Nepal participating in REDD+ pilot projects, focusing on how indigenous peoples, women and Dalits have participated in and been affected by such initiatives. Our research shows that the studied REDD+ pilot activities in Nepal have been, to some extent, able to recognise, empower and benefit certain social groups, indigenous women in particular, whilst Dalits (particularly Dalit women) had a different experience. REDD+ projects have had limited impact in addressing more entrenched processes of political discrimination, male dominance in decision-making, and uneven participation driven by spatial considerations or specific social targeting approaches. While the projects examined here have been partially just, and rather sensitive to existing patterns of social differentiation, the complexity of social differentiation still makes it difficult to operationalise environmental justice in REDD+ implementation. Hence, we conclude that deficits in distributive, recognition and procedural justice cannot be resolved without first addressing wider issues of social injustices throughout Nepal, historically inherited along the dimensions of class, caste, ethnicity, gender, and spatiality.
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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.015 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.017 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".