Learning capacity and diversification, enabling and constraining factors, and external assistance: A cross-national comparative analysis of long-term livelihood recovery
Bibliographic record
Abstract
Despite a wide recognition of the importance of learning capacity and diversification, enabling and constraining factors, and external assistance in facilitating long-term livelihood recovery (LTLR), there is a paucity of comparison for a nuanced understanding of interconnections among the three themes (learning capacity and diversification, enabling and constraining factors, and external assistance) in different societal and disaster scenarios. Accordingly, this article employs a cross-national comparative approach in examining the interplay of these three factors in LTLR, within rural communities, following the two international post-disaster case studies, the 2007 Cyclone Sidr, Barguna, Bangladesh and the 2008 Wenchuan earthquake, Sichuan, China. This cross-national comparison indicates that the affected communities in both cases experienced extreme challenges in LTLR while illustrating the differences. Learning capacity and diversification facilitated asset loss recovery and risk mitigation in the Sidr case, while the Wenchuan case demonstrated a limited learning opportunity for livelihood diversification. Enabling and constraining factors were identified in both case studies. Particularly, people-place connections positively shaped the LTLR in the Wenchuan case while producing negative results in the Sidr case. External assistance facilitated livelihood provisioning, protection, and promotion for the Sidr case; in contrast, giving little, if any, credence to the local traditional livelihood practice, the top-down external interventions in the Wenchuan case jeopardized the rural communities LTLR. This article defends that promoting grassroots participation in community reconstruction and recovery and strengthening grassroots livelihood learning and practice capacities would advance LTLR.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.003 |
| 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".