Urban Growth and Livelihood Transformations on the Fringes of African Cities: A Case Study of Changing Livelihoods in Peri-Urban Accra
Bibliographic record
Abstract
In recent times, a growing body of research has drawn attention to the changing interface and interdependence between urban and rural spaces in Africa. This includes studies on physical, environmental, socio-demographic, economic and other transformations in the peri-urban zone. However, little is known about how residents of peri-urban communities adapt their livelihoods to these transformations. Using the case study approach, and by applying the sustainable livelihood framework as an analytical tool, we have explored the livelihood strategies adopted by the residents of four communities in peri-urban Accrain response to the city’s physical expansion. We find that urban growth has differential effects on peri-urban livelihoods, thereby creating winners and losers. Some residents, by reason of their possession of, or control over, various forms of livelihood assets, are able to utilize opportunities offered by urban growth to devise livelihood strategies to enhance their wellbeing. Those who suffer adverse effects are mainly resource-poor farm households who, apart from not having the wherewithal to take advantage of opportunities created by urban growth, lose their farm-based livelihoods as a result of the conversion of land from agricultural to non-agricultural uses. We therefore recommend that local government authorities should incorporate peri-urban livelihood issues into their planning activities.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.014 | 0.006 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".