Institutional responses to development pressures: Resilience of social-ecological systems in Himachal Pradesh, India
Bibliographic record
Abstract
SUMMARY In the Kullu District, Himachal Pradesh, India, economic and urban growth, and diversification have increased pressure on forests and forest-based social-ecological systems. As in many Himalayan regions, livelihood sustainability is linked to forest resources, products and services. Recent development in the region, to which these systems may be vulnerable, brings into question environmental and livelihood sustainability. This paper examines the resilience of integrated systems of people and nature, or social-ecological systems, in the face of development pressures by evaluating a number of local and state-level institutional responses. Resilience, which describes the ability of the social-ecological systems to adapt to change by buffering shocks, improving self-organization and increasing capacity for learning, is an essential quality for sustainable development. Institutional responses which positively contribute to resilience and sustainability include the work of mahila mandals in forest management, adoption of Joint Forest Management (JFM) policies and practices, upholding rules, strengthening local institutions, establishing firewood depots and adopting alternative energy sources. Institutional failures brought about by the lack of rule enforcement and corruption erode resilience. The analysis of institutional responses helps to identify areas where capacity exists and areas in which capacity building is needed to produce resilient social-ecological systems and therefore, sustainable development. Key words: ResilienceinstitutionsIndiaforestmanagementsustainability
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".