Peripheral Urbanism in Africa: Border Towns and Twin Towns in Africa
Bibliographic record
Abstract
There has been a proliferation of research on Africa’s borderlands over the past decade, which reflects their centrality in regional systems of trade and the rapid growth of border settlements. The development of twin towns/cities at the border, which has attracted the interest of scholars in other regions of the world, has been a distinctive feature of Africa as well. This paper examines some of the particularities of peripheral urbanism in Africa, whilst seeking to avoid a resort to continental exceptionalism. It begins by tracing some broad patterns before homing in on two sets of case-studies along the Uganda/Kenya and Ghana/Togo borders. The paper argues, firstly, for the enduring importance of colonial infrastructural investments and the policy choices that were made after independence. Secondly, it highlights the markedly different variations of scale, ranging from the border capitals of Kinshasa and Brazzaville at one end of the spectrum, through growing towns like Busia-Uganda and Busia- Kenya, to a multiplicity of smaller border settlements at the other end. Thirdly, the paper argues that administrative logics and trade dynamics have been the main drivers in the expansion of twin cities/towns, although the flight of populations from insecurity have also played a significant role in the Great Lakes region and in West-Central Africa. Finally, the paper points to a feature that has been identified in other regions as well, notably the often marked asymmetries between border settlements, which reflects the influence of deeper historical trajectories and contemporary patterns of trade and population movement alike.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".