On criminalized livelihoods and community forestry : a case study of traditional forest use by tribal communities in South Gujarat, India
Bibliographic record
Abstract
In India, the Indian Forest Acts of 1865 and 1878 transferred the ownership of all forest land and its resources to the colonial government. These were replaced by the Indian Forest Act of 1927, currently in force. Successive legislations continued to alienate resources from communities. This inadvertently criminalized the traditional forest-based livelihoods and cultural practices of local tribal communities. Tribal communities continue to follow their local traditions, but lack the voice to protect their culture against non-local interventions. This denial of participation to tribal communities in managing forest resources has led to their alienation from the very resource they depend upon. The Joint Forest Management (JFM) programme was promulgated in 1991 to increase local forest dwellers’ participation in forest management. The Forest Rights Act 2006 (FRA) attempted to address historical injustice meted to forest dependent communities. These policy level initiatives, de jure, could be viewed as an effort to partially restore the primacy of forests in the livelihoods of forest dwelling communities. However, JFM and the FRA continue to be subservient to the Indian Forest Act, 1927, and no substantial change is visible. Using a case-study approach and purposive sampling, four villages in Gujarat State were selected for the study. Data was collected in 1995-97 and 2011 on people’s livelihoods, cultural practices, forest access and restrictions, awareness and participation in governmental, and community forestry activities. These data were compared to ascertain changes and continuity. Results showed that despite JFM, the basic governance of forest management remains unchanged. Forest related cultural practices and livelihoods of local tribal communities continue to be marginalized and criminalized. The thesis argues that the restorative efforts of JFM and FRA fail to adequately uphold human rights of tribal communities. It further offers an analysis of the impact of such provisions on the well-being of the tribal community. Based on a concept of criminalization and empirical findings, the aspect of illegitimate illegality (legitimate activities labelled as illegal) is teased out from corruption. Using elements of Aboriginal Forestry, the Classification of Decriminalization framework is developed to recognize and to respect tribal people’s cultural identities.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.017 | 0.008 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| 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".