POLICIES, COMMUTING PATTERNS AND ACCESSIBILITY IN A NON-MONOCENTRIC CITY: CASE STUDY OF DELHI
Bibliographic record
Abstract
With an estimated 13.8 million people in Delhi in 2001, an overwhelming 93% was urban. Given the highly urbanized character of Delhi, industry, trade and manufacturing offer the maximum employment opportunities for people. In order to study the dynamics of employment distribution over NCT Delhi and to assess the policies outlined for Delhi, certain specific metrics are employed. These include the rank-size distribution and the employment specific preference functions. Results indicate towards the formation of employment centers within Delhi, other than the CBD. Accessibility is an important component of Quality of Life (QoL), which may influence the choice of residential areas. Accessibility indices for different types of land-use activities, i.e. work, education, health and commercial centers are estimated. Accessibility to work has been perceived as the most important by the respondents in Delhi, underpinning the need to investigate job agglomerations in a city where city limits are expanding to accommodate more job opportunities. Transport policies aim to integrate the NCT and the NCR with special focus on the satellite cities, which show high concentration of work centers.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".