Joint forest management in India: implications and opportunities for women’s participation in community resource management
Bibliographic record
Abstract
In recent years, state decentralization of control over community resource management has been increasing on a global scale. This process is largely intended to compensate for bureaucratic inefficiencies through the involvement of local users in state conservation efforts. Since India established its National Forest Policy of 1988, such a shift has occurred in natural resource management from the national to the local level. During the 1990’s this process of decentralization was accelerated under India’s Joint Forest Management (JFM) Policy. This paper examines the implications of JFM in involving local stakeholders with forest management practices, and specifically, women’s role within JFM and the degree of their participation within village forest institutions. Women are the primary collectors of forest products in rural India, and it is recognized that as a forest-dependent group, they ought to be involved in decision-making within these institutions for the sustainability of village livelihoods and conservation efforts. The success of JFM programs in this regard requires that a greater role for women be established through a gender policy within JFM. Both within and outside of state policy, measures to enhance women’s participation must take into account social relations and structures that perpetuate women’s exclusion, and identify ways through which these structures can be transformed. Ultimately, promoting women’s empowerment and livelihood rights and opportunities are essential preconditions to their effective participation.
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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 | 0.009 | 0.006 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".