Bibliographic record
Abstract
In the context of disasters, the term ‘resilience’ is viewed by some humanitarians as overused, underdefined and difficult to operationalise. Moreover, much of this process has been expert- and humanitarian-led, leaving out the understanding of resilience at the local level, among disaster-affected people and in local languages. And when local input from disaster-affected households is included, their understanding of resilience is often filtered through expert and professional opinions. Looking at the case study of resilience-oriented interventions in Tacloban City, Philippines, after Typhoon Haiyan, this study examines local conceptions of resilience by disaster-affected households. Designed and led by local researchers who were also Haiyan survivors, we conducted in-depth interviews with 31 Haiyan survivors in a typhoon-affected community. Results reveal that disaster-affected people have drastically different conceptions of resilience than those promoted by institutions, such as family’s well-being, intactness of the family members after the disaster, durability and having faith in God. Food, financial capacity and psychosocial status significantly influence people’s contextualised meanings of resilience. Access to social and material resources from a household’s social capital networks was also found to be an important factor to understanding resilience.
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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.015 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.014 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.019 | 0.002 |
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".