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Record W4322742818 · doi:10.1177/09754253221151104

‘Aestheticization of Poverty’ and ‘Manufactured Consent’: How Power Imbalances Between Stakeholders Led to the Failure of the Kannankund ‘Model Village’ Housing Rehabilitation Project

2023· article· en· W4322742818 on OpenAlexaff
Meghna Mohandas, Vivek Puthan Purayil

Bibliographic record

VenueEnvironment and Urbanization Asia · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicUrban and Rural Development Challenges
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsBeneficiaryCitizen journalismPovertyPublic housingPower (physics)Urban villageSociologyProcess (computing)Economic growthPublic relationsPublic administrationPolitical scienceBusinessEconomicsEngineeringCivil engineeringFinance

Abstract

fetched live from OpenAlex

In the aftermath of the Kerala floods of 2018, a model village project was proposed in the Kannankund area of Malappuram, Kerala to rehabilitate 34 families who had lost their houses. The project was awarded to a group of technical experts who proposed housing designs that prioritized the aesthetic language of the model village (Roy, 2003). While they engaged in participatory models to establish frameworks for design, the authors of this article observe that the process of participation was largely a smokescreen exercise to ‘manipulate’ beneficiaries into choosing options that were reflective of the aesthetic values central to the project (Arnstein, 1969; Burawoy, 1979). By situating community participation within analytical frameworks of public participation, this article seeks to analyse how skewed power dynamics in beneficiary engagement resulted in circumvention of community needs in the Kannankund model village project. The findings of this article hold significance in informing housing and urban planning practices in projects where participatory processes are being invoked to engage with marginalized communities.

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 score0.216
Threshold uncertainty score0.235

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 head0.041
GPT teacher head0.254
Teacher spread0.214 · 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
Published2023
Admission routes1
Has abstractyes

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