Green Enterprises: A pathway to Women’s Economic Empowerment
Bibliographic record
Abstract
Approximately 80 per cent of Nepal’s rural population, predominantly women, rely on NonTimber Forest Products (NTFPs) for their livelihoods. Despite ample studies on NTFPs, their role in economically empowering women is scant. Taking the case from four community forests in Nawalparasi, this paper examines how NTFPs such as the leaves of Shorea robusta (Sal) and Phoenix loureiroi Kunth (Thakal) are evident means to empower women. To understand how the process empowered women, the researchers emphasise their firsthand observations to serve as valuable data, complemented by key informant interviews (n=55), workshops (n=2), and formal and informal observations.. The results show that the annual harvestable yield of Sal leaves (3 CFs) and Thakal leaves are 207020.75 kg and 5089.35 kg respectively. This would create 448565 days of green employment opportunities for marginalised women and men. The research concludes that involving women in the inventory process aids in enhanced technical forestry knowledge, increased control over access to resources, attitude change, and shifts in power relations Hence, incorporating NTFPs in community forest operational plans and upscaling is recommended to promote economic, technical, and socio-cultural empowerment for both women and men.
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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 | 0.003 | 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 | 0.006 | 0.006 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 0.001 |
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".