Livelihood, Dependence on Forest and Its Degradation: Evidence from Meghalaya
Bibliographic record
Abstract
The linkages between population growth, pattern of livelihood and dependence as well as degradation of forests have long been the subject of debate and concern. It is thus important to investigate to what extent does the growth of population, poverty and livelihood affect the neighbouring forest resources or how are they affected by the degradation of forests. Various assessments have assigned the major responsibility in the loss of forest cover at various places either to population growth or logging, or other commercial resource extraction, including the spread of cattle ranches. The objective of this paper is to unfold the nature of dependence on forest and factors affecting degradation of forest in Meghalaya in an interlinking fashion. The analysis reveals that family size, incidence of poverty, cultivation practice, remoteness of the area and consumption or livelihood pattern have important impact on the extraction of forest resources. Education helps in conservation and sustainable use of the forest resources. Broadly, there is important inter-linkage between population growth, incidence of poverty and degradation of forest in the region.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".