Measuring disaster-resilient communities: A case study of coastal communities in Indonesia
Bibliographic record
Abstract
Vulnerability reduction and resilience building of communities are central concepts in recent policy debates. Although there are fundamental linkages, and complementarities exist between the two concepts, recent policy and programming has focused more on the latter. It is assumed here that reducing underlying causes of vulnerabilities and their interactions with resilience elements is a prerequisite for obtaining resilience capabilities. An integrated approach, incorporating both the vulnerability and resilience considerations, has been taken while developing an index for measuring disaster-resilient communities. This study outlines a method for measuring community resilience capabilities using process and outcome indicators in 43 coastal communities in Indonesia. An index was developed using ten process and 25 outcome indicators, selected on the basis of the ten steps of the Integrated Community Based Risk Reduction (ICBRR) process, and key characteristics of disaster resilient communities were taken from various literatures. The overall index value of all 43 communities was 63, whereas the process and outcome indicator values were measured as 63 and 61.5 respectively. The core components of this index are process and outcome indicators. The tool has been developed with an assumption that both the process and outcome indicators are equally important in building disaster-resilient communities. The combination of both indicators is an impetus to quality change in the community. Process indicators are important for community understanding, ownership and the sustainability of the programme; whereas outcome indicators are important for the real achievements in terms of community empowerment and capacity development. The process of ICBRR approach varies by country and location as per the level of community awareness and organisational strategy. However, core elements such as the formation of community groups, mobilising those groups in risk assessment and planning should be present in all the countries or locations. As this study shows, community resiliency can be measured but any such measurement must be both location- and hazard-specific.
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.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".