‘Aestheticization of Poverty’ and ‘Manufactured Consent’: How Power Imbalances Between Stakeholders Led to the Failure of the Kannankund ‘Model Village’ Housing Rehabilitation Project
Bibliographic record
Abstract
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.
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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 | 0.016 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.026 | 0.040 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.002 | 0.017 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
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.
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".