Message in a Metro: Building Urban Rail Infrastructure and Image in Delhi, India
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract The world over, infrastructure mega projects have become more prevalent, even as evidence suggests that such projects often experience significant cost overruns while failing to fully deliver on their projected benefits. In this light, this article will argue that continued support for infrastructure mega projects stems from the way that such projects are presented to the public. Using the case of the development of a metro railway in Delhi, India, it shows that galvanizing public support and attracting patrons to a public transit system stems from creating an all‐round positive image that combines tangible variables with an intangible set of symbolic meanings. Of course, image is only an impression, and does not necessarily reflect reality. In this light, the final section of this article examines the broad physical and societal implications of the metro development in Delhi, and uncovers the driving forces behind the project. The article concludes that, in spite of the cultivation of a positive image, the specific metro form that was developed in Delhi to satisfy each of the special interest groups involved in its production might be specifically one that fails to suit the transportation needs of the city.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it