Türkiye’s Road to Recovery after the 2023 Kahramanmaras Earthquake: Lessons from Chile, Japan, and Nepal
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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 it