Poverty control policy may affect the transition of geological disaster risk in China
Bibliographic record
Abstract
Abstract The Chinese government has implemented measures to reduce poverty in the country. Specifically, the Targeted Poverty Alleviation (2013–2020) policy is a set of unique, large-scale and precise poverty control measures undertaken by China in an effort to eliminate absolute poverty. Deeply impoverished areas in the mountainous regions of Southwest China are also particularly prone to geological disasters. A poverty control policy might reduce risk from natural disasters in this region by changing human behaviour. However, it is unclear how the risk might change under the government’s poverty control measures. This paper uses power-law relations and negative binomial regression to analyse primary economic losses from geological disasters in Yunnan Province between 2009 and 2017. The results of the analysis show that the relation between the level of economic development and disaster losses in Yunnan Province changed from an inverted-U shape to a U shape in this period. While direct economic losses from geological disasters are falling, we find that losses in wealthy counties Yunnan Province have not decreased significantly and might even be increasing. In impoverished areas, poverty alleviation policies reduce the economic losses of geological disasters by reducing the vulnerability and exposure, and increasing the resilience. On the contrary, poverty reduction measures promote a concentration of population and wealth in non-poor areas, increasing the vulnerability and exposure, which in turn lead to an increase in direct economic losses from geological disasters. Therefore, in order to consolidate the achievements of poverty alleviation projects, the government needs to pay attention to the transfer of geological disaster risk caused by the policy-driven transformation of human social behaviour.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".