Bottom-Up Adaptive Social Protection: A Case Study of Self-Constructed Grassroots Attitude in the Post-Wenchuan Earthquake Recovery
Bibliographic record
Abstract
Current adaptive social protection programs and policies have been predominately designed from the organizational level, applied via a top-down trajectory, and are passively accepted by affected communities. While bottom-up grassroots interventions, providing their benefits, have rarely been encouraged in adaptive social protection programs nor complimented the related adaptive social protection policies. Based on a case study of the post-Wenchuan earthquake reconstruction and recovery in rural areas, this research qualitatively examines the broader range of benefits of self-built undertakings that support government-oriented adaptive social protection initiatives. These self-efforts have accomplished much more than the original adaptive social protection initiatives could have achieved. They not only provide the residents with safe, comfortable, and healthy places to live but also protect their traditional knowledge and skills, improve family relationships, and promote community cohesion. Thus, fundamentally supporting disaster survivors to rebuild their lives and livelihood and strengthen their resilience capacity. Although the uniqueness of the community-based environment limits self-reconstruction, this study argues that the self-reconstruction approach, as a community-driven strategy, encourages communities to develop their instruments, advancing current official adaptive social protection agendas. The bottom-up community-customized interventions will better serve disaster survivors to protect, promote, and transfer affected residents’ livelihoods and social relations; reduce their various vulnerabilities; ultimately build their resilience capacity to achieve the global priority of climate change adaptation and disaster reduction.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.016 | 0.006 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| 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".