Resilience to Weather-Related Disasters of a CBFM Community in Ligao, Albay, Philippines
Bibliographic record
Abstract
This study assessed the resilience of a Community-Based Forest Management (CBFM) community in Ligao, Albay, Philippines to weather-related disasters. Resiliency was measured using 38 indicators comprising human, social, natural, financial, and physical capitals. The study used household survey administered to 180 respondents, complemented by focus group discussions (FGD), key informant interviews (KII), and secondary data gathering. Index of five capital assets was calculated using the equation for data normalization by a scale of 0 to 1. The overall resiliency index was estimated by getting the weighted average of all the capital assets. Pearson Correlation, Chi-square and Spearman Correlation were used to analyze the relationship of age, gender, and household size to the overall resiliency of the community. The CBFM community is less resilient with an index of 0.382. This was attributed to a lesser access to social and natural capitals with indices of 0.233 and 0.244, respectively. However, the CBFM program remains a promising strategy in improving the adaptive capacity of upland communities by contributing to the enhancement of their social and natural assets. Results revealed that there is a positive correlation between household size and resiliency while age and gender were not correlated. To build resiliency, it is recommended to increase community capacity through education and skills development, ensure access to services, provide technical and financial support from the government and promote collaboration among various stakeholders.
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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 | 0.001 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".