A Portrait of the Urban Demographic Profile of an African City—Port Harcourt, Nigeria
Bibliographic record
Abstract
The global population is experiencing a remarkable demographic shift. The population pyramid of African countries looks very different from that of the West, with a youthful population forming the base of the African population, while the population of Western countries has a larger share of an aging population. A broader understanding of the various facets of urban growth in Africa is needed, including the demographic makeup and drivers of growth. However, inadequate attention has been paid to this aspect of urban change in research, even though this knowledge can aid development planning. Demographic concerns like the interconnections between development and population are important issues of national dialogues and debates. Research from Southern Africa has also found a prevalence of female-headed households in urban areas and predicts a rise in this trend. This study thus set out to explore the primary factor behind urban population growth and the extent of prevalence of female-headed households in African cities using Port Harcourt, Nigeria, as a case study. Quantitative research was conducted. The findings revealed that natural increase was largely responsible for urban growth, given the proportion of participants in the age group 18–40 born in the city. This group currently forms the large base of the African urban population. Results also showed that male-headed households were still dominant in Port Harcourt city. This study highlights the need for expansion of similar research in other cities to enable a more holistic understanding of the wider African urban population demographics and dynamics.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".