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Record W2694837311 · doi:10.3138/jcfs.41.3.281

Changing Family Structure in Botswana

2010· article· en· W2694837311 on OpenAlexvenueno aff
Kakanyo Fani Dintwat

Bibliographic record

VenueJournal of Comparative Family Studies · 2010
Typearticle
Languageen
FieldSocial Sciences
TopicPoverty, Education, and Child Welfare
Canadian institutionsnot available
Fundersnot available
KeywordsInheritance (genetic algorithm)FertilityExtended familyNuclear familyPopulationDeveloping countryEmpowermentUnit (ring theory)Economic growthSociologySocioeconomicsGeographyDemographic economicsDemographyDevelopment economicsGenealogyEconomicsBiologyPsychologyHistory

Abstract

fetched live from OpenAlex

Family has been defined as a social unit related by blood, marriage, and adoption. A network of political, social and economic relationship exists among family members, as well as between family members and the society they live in, and this network has influenced the change in family structure. This paper examines the influence of demographic and socio-economic factors on the changing family structures in Botswana comparatively to other Southern African countries. Data from Population Censuses, Demographic Health Surveys and other related literature are used to examine the influence of: fertility, marriage, education, migration, inheritance patterns, and HIV/AIDS on the family structure. It notes that there is a decline in fertility and marriage patterns in Botswana and other Southern African countries under a review which changes the structure of the family from the traditional (extended family) to the modem (nuclear and single-parent families). The paper argues that the weakening of family structure is also because of inheritance disputes which are influenced by inheritance customary laws that look down upon women empowerment. Furthermore, HIV/AIDS related mortality and morbidity has also wrecked a lot of families. There are many orphans in Southern Africa due to HIV/AIDS.

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 score0.415
Threshold uncertainty score0.416

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.057
GPT teacher head0.373
Teacher spread0.316 · 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

Citations37
Published2010
Admission routes1
Has abstractyes

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