Community-Based Approach for Climate Resilience and COVID-19: Case Study of a Climate Village (Kampung Iklim) in Balikpapan, Indonesia
Bibliographic record
Abstract
COVID-19 and climate change are widely recognized to negatively impact communities in developing countries. Like several other developing countries, Indonesia also dealt with climatic hazards such as flooding and landslides during the COVID-19 pandemic. Furthermore, after the Paris Agreement was signed, the government launched a “Climate Village” program or Kampung Iklim (ProKlim) to enhance community contribution in addressing climatic hazard impacts. Yet, numerous studies have researched integrating COVID-19 and climate change impacts, which calls for a concept of community resilience. To bridge this gap, the objective of this research is to understand and measure the local adaptation and mitigation activities in ProKlim through the smart village concept. Methodological literature review, situation analysis through interviews, and field observations are applied in this study. This research used five indicators to measure the current situation of the Climate Village, which are: resilience, mobility, community, perspectives and digitalization. The findings reveal that the implementation of smart villages in ProKlim is still in its preliminary stages and must seek innovation and system integration from smart cities and smart communities. This research also suggests feasible strategies to build community resilience: (i) collaborative governance in the Climate Village program implementation, (ii) promoting the Climate Village program to other sectors for ICT, and (iii) strengthening community participation in implementing the smart village concept.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".