High Speed Railway and its Urban Growth Implications: Public Perception about Factors Inducing Urban Development
Bibliographic record
Abstract
High Speed Railway (HSR) significantly reduces inter-city travel times; hence, it has large-scale implications on urban growth. Many developing countries, like India, are looking forward to constructing HSR. However, the urban growth impact of HSR in these countries will differ from the developed world, requiring newer urban planning methods. Considering public opinion is pertinent for participatory planning. The study analyses factors of an HSR city's urban growth as perceived by ordinary citizens. The rank analysis of the data collected from a questionnaire survey shows that Indians are more concerned about basic amenities than facilities for leisure purposes. Connectivity to HSR stations by metro and proximity to transit stations were the most critical factors for residential growth. Contrary to planners’ view, the multi-functional high-rise commercial development of an HSR station building was perceived as the least important factor for urban growth. Significant differences in the perception among different socio-economic groups were also observed. This study reveals the similarities between the public perception in a developing country and the empirical evidences from developed countries. Also, it highlights the importance of understanding public perception about the effects of transit on urban development.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".