Community vulnerability and capacity in post-disaster recovery: the cases of Mano and Mikura neighbourhoods in the wake of the 1995 Kobe earthquake
Bibliographic record
Abstract
This is a study of how two small neighbourhoods, Mano and Mikura, recovered from the 1995 Kobe (Japan) earthquake, with a particular focus on the relationship between community vulnerability and capacity. Few studies have examined these interactions, even though vulnerability reduction is recognized to be a vital component of community recovery. Drawing from literature on disaster recovery, community development, vulnerability analysis, community capacity building and the Kobe earthquake, a community vulnerability and capacity model is elaborated from Blaikie et al.’s Pressure and Release Model (1994) to analyze the interactions. The Mano and Mikura cases are analyzed by applying this model and relating outcomes to the community’s improved safety and quality of community lives. Based on the experience of Mano, appropriate long-term community development practices as well as community capacity building efforts in the past can contribute to the reduction of overall community vulnerability in the post-disaster period, while it is recovering. On the other hand, the Mikura case suggests that even though the community experiences high physical and social vulnerability in the pre-disaster period, if the community is able to foster certain conditions, including active CBOs, adequate availability and accessibility to resources, and a collaborative working relationship with governments, the community can make progress on recovery. Although both Mano and Mikura communities achieved vulnerability reduction as well as capacity building, the long-term sustainability of the two communities remains uncertain, as issues and challenges, such as residual and newly emerging physical vulnerability, negative or slow population growth and aging, remained to create vulnerability to future disasters. The case studies reveal the interactions of community vulnerability and capacity to be highly complex and contingent on many contextual considerations.
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 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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".