Taiwan Vulnerability Analysis: A Comparative Study with Japan, China, U.S.A., U.K., France, and the Netherlands
Bibliographic record
Abstract
Taiwan has performed well economically during the past four decades. However, economic development can be profoundly hampered by natural disasters. Sustainable economic development requires environmental resilience. With 23 million people occupying only 13,974 square miles of land, Taiwan is both densely populated and highly exposed to natural disasters: 73.1% of the total population lives in vulnerable areas, and Taiwan is ranked as the country most exposed to multiple hazards (The World Bank, 2005). Storms and floods damage Taiwan frequently, with an average of six typhoons hitting Taiwan annually for the past four decades. Taiwan had the highest occurrence and highest death toll on the natural disaster density indicator (NDDI) in comparison with China, Japan, U.S.A, U.K., France, and the Netherlands from 1985 to 2014. Also, Taiwan’s economic losses during the past thirty years are estimated at $650, 000 per km². This is approximately 5 times that of the Netherlands’ $134,362 and the U.K.’s $135,292, 8 times that of the U.S.A.’s $78,186 losses, and 9 times that of France’s $70,599. Research finds that every dollar invested into disaster preparedness would save $4 to $7 dollars in post-disaster damages (Multihazard Mitigation Council, 2005; The National Academy of Sciences, 2012). Hence, promoting urban resilience policies for disaster risk reduction should become a priority in Taiwan and other Asian nations in the future. Most important is the need of a strong political commitment and leadership to initiate and implement spatial policies toward resilience.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".