Achieving Human Security after a Disaster: the Case of the Haiyan Widows
Bibliographic record
Abstract
Abstract When Typhoon Haiyan struck the Philippines on November 8, 2013, it took the lives of over 6,300 people. Many of those who died were men who did not evacuate in order to protect their homes. As a result, widowhood was a significant and devastating consequence of Haiyan, but widowed women were also one of the most neglected and underserved vulnerable populations in the aftermath of the disaster. The data used in this study were drawn from 15 semi-structured interviews and three focus group discussions with widowed women in three areas in the province of Leyte that were heavily affected by Haiyan: Tacloban City, Palo, and Tanauan. Our fieldwork uncovered that while the delivery of humanitarian assistance provided a modicum of human security to the survivors, the ability for widows to achieve human security was severely reduced and constrained. Thus, the main research question of this paper is: “What undermined the widows from attaining human security after Haiyan?” and we argue that there were four main factors: (1) the lack of equal access to economic opportunities; (2) the occurrence of new risks in the resettlement sites; (3) the inability of institutions to respond and adapt to change; and (4) the absence of survivor-centered decision making venues. To overcome these barriers to human security in the future, we make two key policy recommendations on how local government units, being the primary organizations that deliver prevention and response services, need to do. These are: (1) prioritize the elimination of existing economic and social vulnerabilities in the relocation sites, and (2) prepare the widows and their families for future climate shocks.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.022 | 0.008 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.005 |
| 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".