Conserving and commercialising forests: tribal women and subjectivity in Bagafa forest of Tripura (Northeast India)
Bibliographic record
Abstract
Two perspectives on women’s relationships to forests are usually invoked in much of the mainstream policies and research on gender and forest management in India. First, forest-dwelling women, particularly tribal women, are perceived as sharing some inherent and symbiotic relationships to forests and are, custodians of forests. These women are also seen as having significant stakes in state-led forest management projects to meet their subsistence and cultural needs in forests. Second, there is also a perception in India that forest-dwelling communities have strong desires to commercialise forests, and women work with men to garner economic benefits by unsustainably extracting forest resources. Both perspectives are often used to understand how and why tribal women use forest resources and may participate in state-led forest management projects. However, such perspectives give scarce attention to how forest management projects and other lived realities in forests have gendered consequences for women’s lives and livelihoods, the everyday emotional and physical experiences of which reinforce gender inequalities, shape gender subjectivities, and is useful to understand how and why different women may use forest resources and relate to conservation projects in certain complex ways. Through an ethnographic study of a small group of tribal women who illegally planted rubber trees in the Bagafa reserved forest of Tripura, this article examines how women’s subjectivities are produced, performed and contested at intersections of livelihood struggles in forests, aspirations for development, and forest management project-encounters, that come to shape both, their use of forest resources and approaches towards conservation projects.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.011 | 0.009 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.004 |
| 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".