Bibliographic record
Abstract
This paper examines the progress of urban regeneration policies with reference to the Turkish context and a capacity building project experienced in the city of Istanbul.Following the 1999 Marmara earthquake, the Zeytinburnu District in Istanbul was assigned as the 'pilot area for urban regeneration' according to the disaster preparedness policies of the Istanbul Earthquake Master Plan.Following this, the local municipality of Zeytinburnu was determined to demand knowledge and skills for urban regeneration practices.This paper focuses on local practices in comparison with some general trends: changes in local governance and urban regeneration, and the tendency to share out responsibilities through stakeholders via capacity building and physical, economic and legal arrangements, strategies developed with multi-stakeholders through sustainable urban regeneration, strategies for putting theory into practice for the implementation of built local knowledge.The aim of this paper is to reveal the outcomes on 'institutional and community capacity building' in Zeytinburnu as a prior municipality of Istanbul in the means of urban regeneration, and to open a discussion on these outcomes.The paper highlights the importance of capacity building in the disaster preparedness process through outcomes of the Matra REGIMA Project that is in progress in Zeytinburnu Municipality.
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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 | 0.013 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.025 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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 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".