Role of the Public Engagement in the Reconstruction and Revival of the Distressed Urban Textures: Case Study of District 4 in Isfahan, Iran
Bibliographic record
Abstract
Old textures suffer deficiencies in terms of answering the needs of the today’s life due to old age and the resultant weariness and destruction and even the lack of urban facilities and equipment. When the urban life in part of a city becomes stagnant and no efforts are made to regain its boom. The urban texture in that area will be exposed to wearing out. The goal of this study is to review the existing drawbacks and bottlenecks in the process of the revival of the distressed texture of one of the neighborhoods located in District 4, Isfahan, called Kerdabad. The research method is applied in nature and follows a descriptive analytic approach. The findings of the research covers issues such as incompatible applications, resulting in the fact that 80 percent of respondents attribute their dissatisfaction from the environmental conditions of the neighborhood to workshop areas and agricultural fields in the margin of their neighborhood which in turn has resulted in slum life problems in the neighborhood and formation of an inconsistent social context in which 63 percent of the people are dissatisfied of these conditions. While 70 percent of the respondents express their willingness to refurbish their houses, only 73 percent of the respondents attribute proximity to their workplace as the main reason why they prefer to stay in the neighborhood and 89 inhabitants are not well satisfied with the urban services delivered.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.005 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".