Disaster Events and Role Transitions: Narratives of Filipino Rural Women after Typhoon Haiyan
Bibliographic record
Abstract
The Philippines ranks as the third most at-risk country in the world (Radtke et al 2018). It is a disaster-prone country due to its geographical location. On average, the Philippines experiences 20 typhoons annually (Information on Disaster Risk Reduction of the Member Countries n.d.). Typhoons are a commonplace experience in the Philippines. The most frequently hit areas are located in the center, as well as the eastern part of the country. Inhabitants of these areas are inured to these events, therefore, minimal preparations are made. The eastern part of the country is the first to be hit when typhoons enter the Philippine area of responsibility (PAR). On November 8, 2018, one of the strongest typhoons (typhoon Haiyan) to ever hit land devastated the islands of Samar and Leyte (Region VIII) in the Visayas. It left in its wake thousands of casualties and tons of debris. The extent of the damage wrought by the event disrupted the normal day-to-day functioning of the locals (in this case, the women of San Juan, Sta. Rita, Samar). Access to basic needs such as food, shelter, clothing; sanitation, and locals’ sense of safety and security were also disrupted. Aside from the material losses, the socialpsychological aspects of people's lives were also affected, which necessitated major adjustments on their part. One of the vulnerable sectors which was adversely affected by typhoon Haiyan were the women of Barangay (Brgy.) San Juan, Sta. Rita, Samar. The town is located right beside the iconic San Juanico Bridge, which connects the islands of Samar and Leyte. Barangay San Juan is situated in a coastal area which makes it vulnerable to disasters. It is one of the barangays which was adversely affected when Haiyan devastated Region VIII. This chapter expands the idea that disasters expose women's vulnerabilities as they contend with the after-effects of disaster events— in this case typhoon Haiyan. Women are vulnerable to begin with, but the impact of typhoon Haiyan, which resulted in the loss of livelihood, houses, and material things, among others, calls to attention the effects of these losses to women's role transitions and role expansion when they are forced to contend with the challenges associated with these events.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.026 | 0.011 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.003 | 0.010 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".