Challenges to Residential Quarter Reconstruction: The Case of the Center of Yerevan City
Bibliographic record
Abstract
This paper examines the key challenges to the reconstruction of residential development in urban areas. It aims to conduct a comparative analysis of the reconstruction processes of residential quarters in Yerevan and in international practices and to identify certain principles that are appropriate for the further development of the process in Yerevan. The paper presents the features of the reconstruction processes that took place in the sphere of a residential development of the city of Yerevan. The study has been conducted on the reconstruction processes of various nature and content of residential quarters in past decades in a number of cities around the world, focusing on the analysis of topics that remain unexplored in the practice of Yerevan city. A comparative analysis of the findings and conclusions made in the framework of this paper allows us to reveal the strengths and weaknesses in the research and design works for the reconstruction of residential quarters in Yerevan already developed, as well as to develop principles for the choice of study directions, analysis methods, systematization and classification objects, which can be applied to local and international research on the given topic and in the design processes.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.009 | 0.006 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".