Modelling the Roles of Community-Based Organisations in Post-Disaster Transformative Adaptation
Bibliographic record
Abstract
Disasters result where hazards and vulnerabilities intersect. The concept of vulnerability itself is mainly a social construct and the extent to which this can be overcome while transforming disaster-prone systems has often been emphasised in the critical hazard literature. However, the extent to which community-based organisations contribute to post-disaster transformation at the community level remains unexamined. This paper is aimed at examining the extent of the role of community-based organisations (CBOs) in the transformative adaptation of post-earthquake Lyttelton. Quantitative data was obtained from community members using a questionnaire survey of 107 respondents, supporting interviews, and secondary data to explain the phenomenon in this study. System dynamics and agent-based modelling tools were applied to analyse the data. The results show that while CBOs played a major role in Lyttelton’s transformation by fostering collaboration, innovation, and awareness, the extent of their impact was determined by differences in their adaptive capacities. The transformation was influenced by the impacts of community initiatives that were immediate, during, and a long time after the disaster recovery activities in the community. Our research extends the discourse on the role of community-based organisations in disaster recovery by highlighting the extent of CBOs’ impacts in community post-disaster transformation.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".