Enhancing Urban Regeneration and Encouraging Community Participation: Insights from the UAE
Bibliographic record
Abstract
Urban regeneration is an integrated approach that requires the cooperation of specialists from numerous professions, as well as the active participation of the community.Urban regeneration entails not just restoring the energy of declining urban neighborhoods, but also making them more sustainable and environmentally friendly.The rehabilitation of cities initiatives should be based on humancentred programs that promote long-term regeneration of places within the context of sustainable urban expansion.The framework for enhancing regeneration in cities emphasizes the importance of Community Capacity Building (CCB), which aims to empower all members of the community.The study focuses at the Al-Karama neighborhood, which is one of Dubai's oldest urban communities and has undergone many rounds of rehabilitation.The study demonstrates the survey findings for a focus group of residents in order to get their perspective on the rehabilitation projects and context of sustainable urban spaces in their neighborhood.This study aims to provide valuable insights and recommendations to policymakers, urban planners, and community stakeholders by delving into the socio-cultural context and analyzing existing urban development strategies and community engagement frameworks.This study suggests that innovative and impactful approaches to enhance urban regeneration include cultivating cooperation with and inclusiveness sustainable strategies, community involvement, investigating underutilized land resources, and improving urban environment quality.Through a comprehensive exploration of urban regeneration dynamics and community participation mechanisms, this study aspires to offer actionable recommendations and innovative approaches to shape the future trajectory of urban development in the UAE and inspire transformative change on a global scale.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.017 | 0.007 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| 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".