Türkiye’s Road to Recovery after the 2023 Kahramanmaras Earthquake: Lessons from Chile, Japan, and Nepal
Bibliographic record
Abstract
The 2023 Türkiye-Syria earthquake sequence is one of the biggest disasters Türkiye has ever encountered. The loss of lives and properties by the earthquake indicates that the country was not prepared for such a level of disaster. Moving forward, it is critical for Türkiye to prioritize effective disaster recovery efforts and foster a culture of disaster resilience. Disaster recovery is a multifaceted, complex issue that requires effective planning, coordination, well-defined objectives, and community support to succeed. The present study focuses on proposing a recovery framework for affected areas in Türkiye based on a review of existing recovery mechanisms as well as lessons learned from previous recovery efforts in Türkiye and in Chile, Japan, and Nepal. It also includes the outcomes of focused ground discussions and field interviews conducted during the post-2015 earthquake recovery phase in Nepal and during the 2023 reconnaissance study in Türkiye. This study underscores the significance of implementing the “leave no one behind” principle of the Sustainable Development Goals (SDGs) alongside community participation, strong institutional leadership, quality-focused reconstruction, capacity building, building code enforcement, and retrofitting as essential components of an effective and comprehensive recovery strategy. The study serves as a valuable resource for policymakers, decision makers, development partners, and various stakeholders engaged in the recovery process.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".