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Record W2559687758 · doi:10.3968/8926

Street Children in Akwa Ibom State, Nigeria: Br Beyond Economic Reason

2016· article· en· W2559687758 on OpenAlexvenueno aff
Nikereuwem Stephen Ekpenyong, Lawrence Udisi

Bibliographic record

VenueCross-cultural communication · 2016
Typearticle
Languageen
FieldHealth Professions
TopicHomelessness and Social Issues
Canadian institutionsnot available
Fundersnot available
KeywordsPovertyState (computer science)Argument (complex analysis)Developing countryPhenomenonDevelopment economicsSexual abuseEconomic growthPolitical scienceGeographySociologyEconomicsPoison controlSuicide preventionMedicine

Abstract

fetched live from OpenAlex

THE PHENOMENON OF street children has become a global problem. There are as many reasons for being on the street as there are street children. In Nigeria, as in many developing countries, there is a general belief amongst scholars that children “abandon” their families and migrate to the street because of economic poverty. These scholars argue that children whose basic material needs cannot be met within the household move to the street. This paper examines this argument through the analysis of detailed empirical research with street children in Akwa Ibom State, South-South, Nigeria. It found that social factors such as the belief in child witchcraft lie behind most street migration and, in particular, that moves to the street are closely associated with violence to, and abuse of, children within the household and local community. These findings are consistent with the wider literature on street migration from other countries. The paper suggested that in Nigeria, those who seek to reduce the flow of children to the streets need to focus on social policy, especially on how to reduce the excessive control and emotional, physical and sexual violence that occurs in some households. Economic growth and reductions in income poverty will be helpful, but they will not be sufficient to reduce street migration by children in Akwa Ibom State in particular and Nigeria generally.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.052
Threshold uncertainty score0.960

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 communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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.

Opus teacher head0.030
GPT teacher head0.407
Teacher spread0.377 · 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 designObservational
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
Published2016
Admission routes1
Has abstractyes

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