The promises and pitfalls of Hanoi’s urban mobility transition: how residents are navigating the infrastructural politics of a new urban railway
Bibliographic record
Abstract
Low-carbon, urban mobility transitions are being fanfared across the Global South to promote environmental sustainability, while also improving mobility justice. In Vietnam’s capital city Hanoi, an ambitious campaign for such a mobility transition is underway, focusing on creating a ‘green, civilised, and modern’ future. A core feature of this campaign is a new urban railway system. The railway’s first segment, Line 2A, or the Cát Linh – Hà Đông Line, began operations in late 2021 after years of delays and numerous controversies. In this paper we examine how Line 2A has impacted a range of local residents and especially their everyday lived experiences, mobilities, and livelihoods. We draw on conceptual debates regarding infrastructural politics and mobility (in)justices, and ethnographic fieldwork before, during, and six months after Line 2A’s construction. We analyse how local residents have navigated Line 2A’s implementation and early operations, focusing on encounters, entwinements, and tensions that have emerged along the line. We demonstrate how residents’ everyday acts of living with the Line offer insights into how top-down transportation plans and policies are experienced and contested on-the-ground, often deviating from state visions of a ‘civilised and modern’ Hanoi.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.007 | 0.008 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".