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Record W4411256221 · doi:10.4324/9781003480884-5

The Role of International Organisations in Urban Planning in Sub-Saharan African Cities

2025· book-chapter· en· W4411256221 on OpenAlexaboutno aff
Nelson Nyabanyi N-yanbini, Maxwell Okrah, Alfred Toku, Emmanuel Nliwola Bowan

Bibliographic record

Venuenot available
Typebook-chapter
Languageen
FieldSocial Sciences
TopicUrban and Rural Development Challenges
Canadian institutionsnot available
Fundersnot available
KeywordsEnvironmental planningGeographyEconomic geographyRegional scienceUrban planningEconomic growthPolitical scienceDevelopment economicsCivil engineeringEconomicsEngineering

Abstract

fetched live from OpenAlex

The role of international organisations in the design and implementation of urban plans in Africa has gained significant traction in recent years, particularly in installing large-scale infrastructure, development financing, research and innovation. Despite these trends, limited research exists on the influence of international organisations on urban master planning in Sub-Saharan Africa. Through exploratory research design, this chapter evaluates the influence of international organisations on African urban planning by reviewing six urban master plans across Sub-Saharan Africa. To validate the findings from the plans, we interviewed seven experts in urban master planning and international organisation project funding. The findings show the increasing influence of international organisations on urban master planning in the form of technical cooperation, financial investment and knowledge transfer. Japan International Cooperation Agency, the World Bank Group, the Canadian International Development Agency, USAID and China have provided the most technical/financial assistance in African urban planning in the last three decades. However, the complex nature of local participation and the growing interests of government actors in urban planning have significantly reduced this influence. The chapter advocates for broader viewpoints of urban planning aimed at domesticating urban infrastructure finance and technical expertise.

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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.936
Threshold uncertainty score0.999

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 head0.023
GPT teacher head0.258
Teacher spread0.234 · 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 designTheoretical or conceptual
Domainnot available
GenreOther

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
Published2025
Admission routes1
Has abstractyes

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