Understanding the Relationship Between Density and Neighbourhood Environmental Quality – A Framework for Assessing Indian Cities
Bibliographic record
Abstract
The paper brings forth key issues concerning environmentally sustainable development of cities in the wake of rapid urbanization and shows the pathway for future sustainable cities of India. Studies reveal that around the world smaller cities are going to accommodate a larger number of people in the future and be the engines of economic growth and development. A thorough study to ascertain intra-city residential patterns is undertaken. It is perceived that to establish a relationship between residential patterns based on built-forms, distribution of dwelling units, population distribution, etc. (collectively known as physical density) and environmental quality, it is quintessential that local environmental problems are studied at the neighbourhood level. Following this, the terms density and environmental quality are defined and common measures adopted to describe the different types of physical density and indicators to assess neighbourhood environmental quality (NEQ) are identified. The literature review reveals that studies taking into account physical aspects of the built environment and their impact on urban environmental quality (UEQ) are sparse especially in the Indian context, thus justifying the scope of the work. The study concludes with the discussion of impacts of increasing density on environmental quality and identification of a set of variables as emerging from the literature review to help formulate an adaptive indicator framework for assessing NEQ in Indian cities.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".