Natural resource management philosophy: Sustainability principles in forest area management local community-based
Bibliographic record
Abstract
Background: The application of philosophy in natural resource management provides a solid foundation for making wise and sustainable decisions. Forests are a source of livelihood for people, especially in developing countries including Indonesia. Policies that favor the fulfillment of human needs without damaging the environment are based on the philosophy of environmental ethics and sustainability. Each country has different policies in managing forest areas, including involving local communities. Methods: This article analyzes community-based forest area management implemented by a number of countries such as Indonesia, Thailand, Myanmar, Bangladesh, Canada, and Mexico. This article also examines the application of the concept of collaboration and a stronger role of the private sector in other countries. The in-depth analysis in this article uses literature and case studies from Indonesia, Thailand, Myanmar, and Bangladesh. Canada, the United States, and South Korea. Findings: The article's conclusion highlights that sustainable natural resource management hinges on applying philosophical principles, particularly environmental ethics and sustainability, to policy-making. It emphasizes that community involvement and robust governance are key to successful forest conservation efforts, as shown by various case studies and management models. Conclusion: Forest management is greatly influenced by the relationship between state capacity and social capital, in this case community participation. If the capacity of the state is weak, while social capital is weak, then the concept of community-based forest management can be carried out. Novelty/Originality: The rehabilitation of an area of 410 ha into rubber plantations managed by local residents has brought in new sources of income. From 2010 to 2017 the Gini coefficient of inequality decreased from 34.6% to 31.3%.
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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 | 0.005 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.017 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".