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Record W4319841695 · doi:10.1177/02637758221148733

Experiments in peripheral urbanization: Building and unbuilding commons in urban India

2023· article· en· W4319841695 on OpenAlexaff
Priti Narayan

Bibliographic record

VenueEnvironment and Planning D Society and Space · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicWater Governance and Infrastructure
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsCommonsUrbanizationPoliticsSociologyLegitimacyAutonomyRight to the cityUrban studiesEconomic growthPolitical scienceLawEconomics

Abstract

fetched live from OpenAlex

Peripheral urbanization is the predominant mode of producing space in the Global South, in which residents build their own homes and neighborhoods, becoming citizens and political agents in the process. In this article, I bring feminist ethnographic attention to community infrastructure such as childcare centers built collectively by women residents in MGR Nagar, an informal urban settlement in Chennai, India, as understudied examples of autoconstruction in peripheral urbanization. Marxist feminism enables a theorization of these infrastructures of social reproduction as urban commons that assert collective spatial autonomy and enable moral claims on urban space, while serving the everyday needs of its residents. The subsequent demolition of the childcare center caused symbolic and material loss to residents. However, the ceding of territorial autonomy and spatial privileges was a way for them to make new material and political gains in the city, suggesting that a feminist politics of space is possible in which legitimacy and responsibility are demanded from the state. The commons in turn can be seen as durable countertopographies enabling a politics of place in multiple locations.

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 score0.098
Threshold uncertainty score0.365

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 head0.014
GPT teacher head0.257
Teacher spread0.243 · 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

Citations12
Published2023
Admission routes1
Has abstractyes

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