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Importing Problems: The Impact of a Housing Ordinance on Colombo, Sri Lanka

2005· article· en· W2211093508 on OpenAlexvenueno aff
Nihal Perera

Bibliographic record

VenueArab world geographer · 2005
Typearticle
Languageen
FieldSocial Sciences
TopicUrban Planning and Governance
Canadian institutionsnot available
Fundersnot available
KeywordsColonialismOrientalismNegotiationIdeologyIndigenousViewpointsSociologyPower (physics)Sri lankaPolitical economyPolitical scienceLawHistoryPoliticsEthnologySouth asiaArchaeology

Abstract

fetched live from OpenAlex

Edward Said exposes how the U.S. stereotype of the Orient is constructed, hegemonized, and reproduced. The cities that the scholars talk about, the administrators administer, and the planners plan are also perceptions. This article investigates the construction of the perception of low-income areas in Colombo, Sri Lanka, as problems by its British colonial authorities in the 1910s–20s. It undertakes the cultural “unpacking” of this continuing colonial discourse. The article focuses on how the “concrete” living environments that had existed for many decades were re-presented as problems and as objective knowledge. It addresses a conflict and negotiation between two European groups: the British and British colonial authorities in Colombo. I argue that the tipping point of this transformation was the introduction of the Housing Ordinance of 1915 and that the transformation has more to do with British town planning discourses, of which the ordinance is a part, than with local conditions or indigenous or colonial viewpoints. However, this social production of urban problems must be seen within layers of power stemming from the imperial-colonial structures but mediated by regional officers who varied the practice of colonialism while maintaining the ideology of the “orientalist discourse.” It demonstrates that planners and authorities do not have a privileged vantage point to view the city, nor are their positions superior.

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)0.001
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 score0.282
Threshold uncertainty score0.994

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.020
GPT teacher head0.302
Teacher spread0.282 · 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

Citations2
Published2005
Admission routes1
Has abstractyes

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