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Record W4393167568 · doi:10.3138/jcfs-083-2023

The State of Urbanization, Demographic Changes, and Family Dynamics in Africa

2024· article· en· W4393167568 on OpenAlexvenueno aff
Ahmed Aref, Angela Fallentine, Sarah Zahran

Bibliographic record

VenueJournal of Comparative Family Studies · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicDemographic Trends and Gender Preferences
Canadian institutionsnot available
FundersUniversity of Pretoria
KeywordsUrbanizationState (computer science)Demographic economicsGeographyEconomic geographySocioeconomicsDevelopment economicsEconomic growthSociologyEconomics

Abstract

fetched live from OpenAlex

Rapid urbanization and population growth in Africa, coupled with the complex interplay between changing demographics, have resulted in significant implications for families. Using secondary sources, this desk review explores macro-level population dynamics and demographic shifts surrounding family size, intergenerational solidarity, housing, care for the elderly, marital relationships, declining marriage rates, and rising divorce rates. It draws from the demographic transition theory to underscore the impact of social and cultural factors on fertility rates and family dynamics. This article further underscores the significance of these dynamics in the context of North African societies and the broader African continent, offering insights into the evolving role of families and their unique challenges. It then discusses the missed opportunities associated with the demographic dividend and youth bulge, and the measures necessary to unlock the region’s full potential for sustainable economic growth and social development. The article concludes with policy recommendations for strategic development planning, investments in human capital, rural development, and research to navigate the complex connections between urbanization, demographics, and family dynamics.

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)0.001
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: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.255
Threshold uncertainty score0.661

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
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 head0.109
GPT teacher head0.359
Teacher spread0.249 · 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 designQualitative
Domainnot available
GenreEmpirical

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

Citations8
Published2024
Admission routes1
Has abstractyes

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