Effect of Forest Rights Acts 2006 on the Livelihoods of Tribals: An Empirical Study of the Juang Tribe in Keonjhar District, Odisha
Bibliographic record
Abstract
In response to the impacts of external factors on forest vegetation, biodiversity, and the livelihoods of forest-dwelling tribes, various policies have been established to conserve these forests.The Forest Rights Act of 2006 is one such policy.However, this act's unclear implementation procedures and overlap with the Indian Forest Act of 1927 have attracted significant criticism.Limited studies have assessed the impact of FRA 2006 on the livelihoods of the resident tribes.To fill this gap, this study aims to examine the effects of the Forest Rights Act on household income sources, using data from the Juang tribe in Keonjhar District.A range of statistical analysis tools, including correlation, partial correlation, and multilevel regression models, were employed.Data were gathered through structured interviews from 117 tribal households belonging to the Juang tribe in Odisha.The results highlighted the importance of agricultural income for tribal households.The correlation result reveals that landholding size (r = 0.811) and the number of earning members (r = 0.764) were strongly correlated with household annual income.However, the existing laws and strategies do not directly foster income growth.The findings emphasise the need to enhance laws in a manner that respects local conditions and cultural contexts to help reduce poverty.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 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".