Making Cities Resilient in Ghana: The Realities of Slum Dwellers That Confront the Accra Metropolitan Assembly
Bibliographic record
Abstract
Over the past three decades, various countries and stakeholders have aimed at having cities that can better handle natural and human-made disasters, protect human life, absorb the impact of economic, environmental and social hazards and promote well-being, inclusive and sustainable growth. This paper investigates how informal ties result in in-filling and the creation of slums in the context of efforts to make cities resilient in Ghana using the Accra Metropolis as case study. The United Nations Habitat classification of slums was used to purposively select two slum settlements in Accra for the study. The study used mixed methods of quantitative and qualitative approaches to collect data from April 2018 to August 2018. Quantitative data was collected from 400 slum dwellers while qualitative data was collected from eight focus group discussion sessions and in-depth interviews with at least one senior official from related institutions such as Accra Metropolitan Assembly (AMA), Ministry of Local Government and Rural Development (MLGRD), Ministry of Water Resources (MWR), Ministry of Works and Housing (MWH), Ministry of Inner City and Zongo Development (MICZD), Environmental Protection Agency (EPA), Ghana Police Service, and Ghana National Fire Service. Descriptive techniques were used for the analysis. The findings are that informal ties contribute to in-filling in slums. Slum dwellers do not plan to return home, they are not involved in land use decision making and the slums have opportunities and challenges to the slum dwellers and AMA. The AMA should avoid forced eviction of slums and rather enforce development control bye-laws, implement slum upgrading programs, and involve slum dwellers in upgrading programs. Slum dwellers must cooperate with AMA to make Accra resilient. The mainstreaming of the issue of slums in all urban development agendas needs to be given the needed political and policy attention by central government and all stakeholders.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.010 | 0.007 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| 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".