Empty pantries: The death of survival myths among typhoon Haiyan survivors in resettlement sites during COVID-19
Bibliographic record
Abstract
Research has shown that globally, people often rely on their social networks to survive and recover from crises and disasters. This phenomenon has also been seen in the Philippines, a nation well-acquainted with disasters. This paper will look at the role social capital has played in the survival of an understudied and vulnerable group in the Philippines during the COVID-19 pandemic - the survivors of Typhoon Haiyan (locally known as Typhoon Yolanda) living in resettlement sites north of Tacloban City. Using a qualitative approach to analysis 357 household surveys of resettlement site residents across Tacloban City, we argue that the myth of social capital as a lifeline for Filipinos in times of disaster does not necessarily apply to Haiyan survivors in resettlement sites during the pandemic. The paper finds that respondents’ relational social capital is imbalanced, with Haiyan survivors relying mainly on themselves or their bonding ties like close family members for financial assistance during the pandemic. The lack of diversity in their relational social capital in turn impacts their ability to survive and recover from COVID-19. Meanwhile, the relevance of collective social capital among respondents during the pandemic is not clear either – participants see value in engaging in mutual support initiatives (i.e., bayanihan) and share relatively strong relationships with some members of their community but also possess low levels of trust toward fellow resettlement site residents.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".