Integrating Evidence-Based Management Principles with Green Economy in Kampung Tematik
Bibliographic record
Abstract
This study aims to improve the management of Kampung Tematik (thematic village) through the application of evidence-based management (EBM) to optimize social, economic, and environmental outcomes in supporting the green economy.The methods used include semistructured interviews with twenty-five Kampung Tematik leaders Focus Group Discussion (FGD) with nine Tangerang City Regional Development Planning Agency participants, and direct observations.Data were triangulated to strengthen the analysis.The data were analyzed using Grounded Theory through open, axial, and selective coding to develop a framework that integrates EBM and the green economy.The study's findings show that effective waste management and greening initiatives are crucial in reducing emissions and increasing economic value through community participation.Additionally, participatory decision-making plays a significant role in ensuring sustainable governance and equitable resource distribution.Other emerging themes include emission reduction, economic value, social equity, and EBM principles.In conclusion, EBM encourages collaboration, decision-making, and continuous improvement in organizational practices.This study underscores that the integration of EBM within Kampung Tematik governance fosters collaboration, data-driven decision-making, and continuous improvement in sustainability practices.These findings hold broader implications for policymakers and community leaders by demonstrating how local sustainability initiatives can contribute to achieving SDGs.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".