Community-Based Approach for Climate Resilience and COVID-19: Case Study of a Climate Village (Kampung Iklim) in Balikpapan, Indonesia
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 it