Urban Informality, Housing Insecurity and “Bulldozer Urbanism” in Global South Cities: Evidence From Selected Slum Communities in Accra, Ghana
Bibliographic record
Abstract
ABSTRACT In Accra, state‐led eviction mirrors ongoing processes of socio‐spatial inequality and exclusion. While evictions are rooted in neoliberal ideals, the outcomes of such processes have been particularly devastating for residents of slums and informal settlements. This paper uses Cernea's Risk and Reconstruction Model to examine the impacts of bulldozer urbanism on three selected slum communities in Accra. Bulldozer urbanism is rationalized by municipal authorities as an approach to sanitize urban environments by removing what is perceived as filth, dirt, and a looming environmental hazard. While Cernea's model was valuable in uncovering the multiple impacts of evictions on our study communities, the findings reveal outcomes that extend beyond its scope by situating the findings within the broader discussion of state power, neoliberal governance, and urban dispossession. The paper highlights the urgent need for policymakers to embrace and recognize slums and informal communities as integral contributors rather than obstacles to urban development. Based on the findings, the study advocates for a shift from “bulldozing” to upholding the housing rights of slum dwellers as an important step for realizing just, equitable and inclusive cities.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".