A pathway to initiate bottom-up community-based disaster risk reduction within a top-down system: The case of China
Bibliographic record
Abstract
In China, as in other countries, inadequate knowledge of local vulnerability and hazard characteristics, and a rapidly industrialising society render enhancing resilience to natural disasters particularly challenging.This is particularly evident in rural areas with limited human and financial resources available for disaster risk reduction initiatives.The Chinese government institutionalized a top-down community-based disaster risk reduction (CBDRR) system to ensure that the capacity of communities would be enhanced effectively.In the long run, a top-down management style often undermines local capacities and vernacular DRR (disaster risk reduction) knowledge.There is a need to recognize the importance of communities as complex and dynamic entities in reducing disaster risks.Adopting participatory action research (PAR), this in-progress exploratory study examines a pathway to initiate bottom-up CBDRR within China's top-down institutional setting.Through PAR, the study of a rural village in Shaanxi Province shows that bottom-up initiatives can complement the existing system.Its current progress demonstrates the potential for using a transdisciplinary perspective to initiate CBDRR in China, where both top-down and bottom-up approaches, come together alongside different disciplines to increase a rural community's disaster 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 | 0.006 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.016 | 0.008 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".