IMPACT OF SOCIAL FACTOR: REVITALISATION OF LIVABLE ENVIRONMENT OF THE RESIDENTIAL HOUSING IN BAKU CITY
Bibliographic record
Abstract
Most people can agree that cities are places where large numbers of people live and work. they are hubs of administration, commerce and housing. By 2030, a projected 27 per cent of people worldwide will be concentrated in cities with at least 1 million inhabitants. Because of geopolitical options, Baku as capital and one of the post-soviet city went through many challenges in term of economic platform, public mentality, and build environment. Baku demographic dominance means that the city contains the largest group of poor (one quarter of the total). Actual processing of housing transformation inside of the city is segregation by economic status. From 2017, the new districts for very rich peoples are emerging, generally with the territory fenced by high wall. Thereafter evidently, there is a growing process of dissatisfaction around middle and creative class.Livability refers to the subset of sustainability impacts that directly affect people in a community, such as economic development, affordability, public health, social equity and pollution exposure. Azerbaijan has also endorsed Transforming our World The 2030 Agenda for Sustainable Development (2030 Agenda), which entails 17 Sustainable Development Goals (SDGs) and 169 associated targets, that were formally approved by the Member States on 25-27 September 2015 at the United Nations Summit on Sustainable Development and entered into force in January 2016. This paper tackles the problem of misbalanced environment in livable area in Baku City.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".