Conclusion (For Now): Post-Haiyan Philippines and Beyond
Bibliographic record
Abstract
Super typhoon Haiyan or Yolanda left such profound realities of global climate change, destruction, failures, and foresight of a disaster since November 8, 2013. It was first recorded to hit Palau and Micronesia before making landfall as a Category 5 storm in the Philippines (World Vision 2022). Considered one of the worst disasters in recent decades with 313kph winds and 7m waves in the Philippines (BBC Bitesize n.d.), typhoon Haiyan also made landfall in Vietnam on November 11 as a tropical storm with less intensity; other countries affected included China and Cambodia (Al Jazeera 2013; OCHA 2013). In the Philippines, typhoon Haiyan resulted in over 6 million displaced people, 1.9 million homeless, more than 7,000 deaths, and an economic impact of US$5.8 billion (BBC Bitesize n.d.). We may use many superlatives around Haiyan and the fact remains it is only an example of what unfolds in our midst— a disaster. Disasters intersect with social, political, and other dimensions of power that make vulnerable people and communities at more risk from natural hazards and continuing environmental degradation in the age of the Anthropocene. We acknowledge the effects of human acts that have put the Earth in peril. In this collection, we have focused on typhoon Haiyan, before and after , as a sort of midway discourse of the past disasters that occurred in the Philippines that remained unlearned to keep the country disaster resilient as its fate as one of the most at-risk countries in the world for typhoons, floods, earthquakes, and volcanic eruptions. Typhoon Haiyan points to moving ahead with the challenges of island geographies, political will, centralization, bureaucratic (in)competencies, disaster risk reduction and management, humanitarian, and post-disaster development response, amongst others. All the chapter contributions in this book are concrete illustrations of the economics, sociology, politics, psychology, and governance that shape the development landscape post-disaster of the affected population, communities, institutions, and the international community. The interplay of disaster risk reduction and climate change adaptation is invariably influenced by the capacity in disaster risk proofing development agenda, the cultural production of hazards, the geophysical and environmental characteristics, and timely response.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.008 | 0.012 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.004 | 0.010 |
| Insufficient payload (model declined to judge) | 0.068 | 0.014 |
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".