Fixing motorisation: The logics of infrastructure solutionism in Bengaluru
Bibliographic record
Abstract
Cities often deploy infrastructure-based solutions to tackle problems such as congestion caused by increasing motorisation rates. Such solutions include the introduction of complete streets or improved public transit systems. However, these solutions are often viewed as ‘quick fixes’ that are expected to resolve issues with ease. This article examines this phenomenon, which we call infrastructure solutionism, through two case studies in Bengaluru, India – re-shaping public transportation to attract car users through demand management, and redesigning major streets to accommodate varied users through parcelling. Through these case studies, it becomes evident that infrastructure solutions did not address the problems caused due to motorisation. Building upon the literature on technological solutionism in Science and Technology Studies, this article unpacks rationalities of infrastructure solutionism by examining material, valuational and expectational commitments mobilised through each case, and suggests that such solutions appear to be concerned with city image building, rather than addressing the chokehold of automobilisation.
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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) | 0.000 | 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 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".