The Impact of Deforestation on Sustainable Development Goals Regulations: An Empirical Studies on Tawangmangu
Bibliographic record
Abstract
Deforestation represents a permanent transition from forested to non-forested areas, primarily driven by human activities. Such significant land conversion has occurred along the Tawangmangu alternative road for the development of ecotourism. In 2015, the United Nations officially endorsed the Sustainable Development Goals (SDGs) Agenda, comprising 17 Goals and 169 Targets, expected to be achieved by 2030. In Indonesia, the SDGs were ratified in Presidential Regulation Number 59 of 2017 regarding the Implementation of Achieving SDGs. This research employs a qualitative method, examining the rate of conversion from forest to buildings along the Tawangmangu alternative road and using legal protection theory to understand steps to prevent deforestation. The Tawangmangu District Government should apply both preventive and repressive protections, including socialization about the SDGs and warnings about the importance of a Building Permit (IMB), as well as imposing administrative sanctions against law-violating buildings. Although this study contributes significantly to the SDGs field, its major limitation is the small-scale sample, particularly along the Tawangmangu alternative road. Future research could address this by expanding the sample size and further exploring the benefits of SDGs implementation.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".