Discourse of Military-Assisted Urban Regeneration in Colombo: Political and Elite Influences on Displacing Underserved Communities in Postwar Sri Lanka
Bibliographic record
Abstract
This study examines the political and elite motives behind Colombo’s ‘world-class city’ initiative and its impact on public housing in underserved communities. Informed by interviews with high-ranking government officials, including urban planning experts and military officers, this study examines how President Rajapaksa’s elite-driven postwar Sri Lankan government leveraged military capacities within the neoliberal developmental framework to transform Colombo’s urban space for political and economic goals, often at the expense of marginalized communities. Applying a contextual discourse analysis model, which views discourse as a constellation of arguments within a specific context, we critically analyzed interview discussions to clarify the rationale behind the militarized approach to public housing while highlighting its contradictions, including the displacement of underserved communities and the ethical concerns associated with compulsory relocation. The findings suggest that Colombo’s postwar public housing program was utilized to consolidate authoritarian control and promote speculative urban transformation, treating public housing as a secondary aspect of broader political and economic agendas. Anchored in militarized urban governance, these elite-driven strategies failed to achieve their anticipated economic objectives and deepened socio-spatial inequalities, raising serious concerns about exclusionary and undemocratic planning practices. The paper recommends that future urban planning strike a balance between economic objectives and principles of spatial justice, inclusion, and participatory governance, promoting democratic and socially equitable urban development.
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 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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.015 | 0.018 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".