Partitioning (Facade) and Identity in the Historical Context Case of Zonouz City
Bibliographic record
Abstract
City, identity, and urban landscape are interconnected with each other. Urban restoration is the final objective of the study and implementation of partitioning in historical contexts. The identity of city and its determining urban elements were always discussed while the urban passages are the most prominent elements in creating the historical context of urban landscape. The present study aimed at investigating the implementation of favorable partitions by considering vitality in historical contexts. This study analyzed the identity, originality, and partitioning to apply the principles defining a logical solution and creating the sense of belonging, collective memories, sense of place, and points related to environmental psychology in the historical contexts. Using some landscape architecture principles by considering some parameters such as originality, identity, and social-cultural elements can contribute to idealizing the identity partitioning to historical contexts and its adjacent points in urban areas. As a result, the identity concepts and parameters can interpret the partitions in terms of thought, design, and implementation of the principles used in the architecture.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".