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Record W4200443181 · doi:10.1111/1468-2427.13077

<scp>AIMING FOR THE</scp> ‘<scp>GREEN</scp>’: (Post)Colonial and Aesthetic Politics in the Design of a Purified Gated Environment

2021· article· en· W4200443181 on OpenAlexfundno aff
Devra Waldman

Bibliographic record

VenueInternational Journal of Urban and Regional Research · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicSport and Mega-Event Impacts
Canadian institutionsnot available
FundersSocial Sciences and Humanities Research Council of CanadaMitacs
KeywordsPoliticsAestheticsColonialismArchitecturePower (physics)SociologyField (mathematics)Environmental ethicsPolitical scienceLawVisual artsArtPhilosophy

Abstract

fetched live from OpenAlex

Abstract This article investigates the politics of the design of a golf‐focused gated community in Gurgaon, India. It considers the aesthetic uses of golf and architecture that go into the production of a purified urban environment to explore the relationship between urban development, environmental aesthetics and spatial purification in contemporary India. I demonstrate how an architectural focus on golf reproduces the ‘distribution of the sensible’ by attempting to delimit the field of view: who and what is seen, and what an individual can or cannot see. I show how golf is political—deeply connected to and inseparable from legacies of colonial environmental and spatial purification and exclusion, as well as contemporary aesthetic‐political regimes that justify spatial segregation, cleansing, and the protection of beautiful environments away from the urban poor. The aesthetic emphasis on a beautified, green and empty environment that characterizes the production of golf highlights the aesthetic terms on which environmental selves are imagined and how environmental images are constructed. This is an aesthetic premised on the creation of shared viewership combined with the power to be(long) in a place where one can be with others but not mixed up with them.

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)0.004
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.816
Threshold uncertainty score0.227

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.142
GPT teacher head0.388
Teacher spread0.246 · 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 designNot applicable
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

Citations10
Published2021
Admission routes1
Has abstractyes

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