Political Economy and Urban Poverty in the Developing Countries: Lessons Learned from the Sudanese Experience
Bibliographic record
Abstract
Urbanization is rapidly growing all over the world with a high rate of growth in the developing countries. Several factors are presumed to be responsible for the unfolding of urban growth and urbanization processes, which include but not limited, massive rural-urban migration, imbalances in the provision of social services, change in land use policy, market liberalization, conflict, wars, natural disasters, and the consequences resulting from deficient economic policies and attendant practices. In a country like Sudan, rapid urban growth has a propensity of culminating in reduction of job opportunities, which is coupled with high cost of living that subjects urban dwellers particularly the vulnerable group to be plunged into deep poverty. This paper deals with the issue of urban poverty in the developing countries taking Sudan as an example. The overall objective is to uncover the current situation of urban poverty with particular emphasis on its pattern, causes, indicators and the policies adopted for tackling and alleviating urban poverty. By so doing, it is expected that this piece could help in bridging some gaps regarding the understanding and knowledge with regard to urban poverty. The political economy approach is used in order to link political and economic policies at both national and international levels. This approach shows that the urban poor in developing countries tends to be further impoverished mainly as a result of defective economic policies rather than other causative factors. It is believed that the Structural Adjustment Program (SAP) prescribed by the World Bank could be held responsible for pushing a considerable number of urban dwellers into poverty. Since their accession to independence, most of the developing countries are induced by alleged benefits that could accrue from subscribing to western economic policy prescriptions that are in most cases irrelevant to the need of local communities. The establishment of big projects under the pretext of “new development” has always been a political decision that is shaped by the interests of national and international policymakers rather than local priorities and needs.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 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".