Post-disaster social capital: trust, equity,<i>bayanihan</i>and Typhoon Yolanda
Bibliographic record
Abstract
Purpose The purpose of this paper is to explore the impact of disaster rehabilitation interventions on bonding social capital in the aftermath of Typhoon Yolanda. Design/methodology/approach The data from the project are drawn from eight barangays in Tacloban City, the Philippines. Local residents and politicians were surveyed and interviewed to examine perceptions of resilience and community self-help. Findings The evidence shows that haphazard or inequitable distribution of relief goods and services generated discontent within communities. However, whilst perceptions of community cooperation and self-help are relatively low, perceptions of resilience are relatively high. Research limitations/implications This research was conducted in urban communities after a sudden large-scale disaster. The findings are not necessarily applicable in the rural context or in relation to slow onset disasters. Practical implications Relief agencies should think more carefully about the social impact of the distribution of relief goods and services. Inequality can undermine community level cooperation. Social implications A better consideration of social as well as material capital in the aftermath of disaster could help community self-help, resilience and positive adaptation. Originality/value This study draws on evidence from local communities to contradict the overarching rhetoric of resilience in the aftermath of Typhoon Yolanda.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".