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Record W7117109811 · doi:10.1080/23800127.2025.2600712

The Kochi metrorail project: transforming the identity of the passenger

2025· article· en· W7117109811 on OpenAlexafffund
Sreelakshmi Ramachandran, Yogi Joseph

Bibliographic record

VenueApplied Mobilities · 2025
Typearticle
Languageen
FieldEngineering
TopicUrban Design and Spatial Analysis
Canadian institutionsConcordia University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsIdentity (music)Identification (biology)Field (mathematics)Ethnic group

Abstract

fetched live from OpenAlex

While transport infrastructures have received scholarly attention as markers of aspiration and world-classness, their role in reordering the urban space has received scant attention. Transport spaces are important intermediary places, acting as potent venues for production and day-to-day consumption of urbanity. The sensorially intense atmospheres afforded by transport infrastructures present numerous opportunities for the intermingling of mobile bodies while challenging the status quo. Through a combination of design decisions and regulatory interventions, planners shape the production of atmospheres orienting transit users to a redefined, rarefied public ethic. Attending to the growing interest in public transport as a contentious public space, we deploy ethnographic tools of participant observation, conversations and interviews to interrogate the Kochi metrorail project in India while analysing the material and nonmaterial aspects of the system. Through an analysis of spatial practices in metrorail spaces, viewed through the lens of infrastructural citizenship and reinforced by project imagery and messaging, we chart the reconfiguration of the model passenger within the broader framework of metrorail exceptionalism. We argue that fostering citizenship and sustainability using public transport entails minimizing access barriers, embedding metrorail within urban socio-political contexts, and enabling the continuity of informal street practices across metrorail spaces.

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.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.684
Threshold uncertainty score0.201

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.006
GPT teacher head0.203
Teacher spread0.197 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes2
Has abstractyes

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