<scp>AIMING FOR THE</scp> ‘<scp>GREEN</scp>’: (Post)Colonial and Aesthetic Politics in the Design of a Purified Gated Environment
Bibliographic record
Abstract
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.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.027 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".