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Record W3164388345 · doi:10.21203/rs.3.rs-496730/v1

Fantasy visions, Informal Urbanization, and Local conflict: Contradictions of Smart City imaginaries in India

2021· preprint· en· W3164388345 on OpenAlexaff
Debadutta Parida

Bibliographic record

VenueResearch Square · 2021
Typepreprint
Languageen
FieldEngineering
TopicSmart Cities and Technologies
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsVisionFantasyUrbanizationSociologyEconomic geographyPolitical scienceGeographyEconomic growthArtEconomicsAnthropologyLiterature

Abstract

fetched live from OpenAlex

Abstract Smart city imaginaries have emerged in southern cities driven by neoliberal logics in the urban space. In the Indian context, much scholarly work in India has continued to engage with sweeping accounts of cities as opposed to detailed empirical studies. In this paper, I attempt to address this gap through an in-depth ethnographic inquiry of a slum redevelopment project(part of Smart city initiative) in the city of Bhubaneswar, India. I seek to understand ways in which smart city initiatives influence the everyday life of the marginalized urban slum dwellers. Drawing on participant observation; document analysis; and semi-structured interviews, I put forth three key findings from the study that advance the notion that Smart Cities initiatives in Indian cities actively marginalize the slum dwellers by attempting to dominate through inclusion. I borrow from Foucauldian concept of ‘counter-conducts’ and its reimagination in planning by Huxley (2017) to demonstrate that smart cities discourses are counter-intuitively resulting in emergent spaces of resistance in the form of counter-hegemonic practices, thus allowing spaces for evolution of discourse from unfamiliar territories. I conclude by discussing that city planning and governance pathways in India risk creating complicated path dependencies that can lead to state-citizen conflicts in the future.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.033
Threshold uncertainty score0.760

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.001
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 head0.025
GPT teacher head0.302
Teacher spread0.277 · 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 designObservational
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
Published2021
Admission routes1
Has abstractyes

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