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Record W2592824661 · doi:10.1504/ijmrm.2015.074176

A journey to the south: socio-economic implications for young female head porters in the central business district of Kumasi, Ghana

2015· article· en· W2592824661 on OpenAlexaff
Kwadwo Afriyie, Kabila Abass, Micheal Boateng

Bibliographic record

VenueInternational Journal of Migration and Residential Mobility · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicMigration and Labor Dynamics
Canadian institutionsBrock University
Fundersnot available
KeywordsLivelihoodEmpowermentEconomic growthWork (physics)Intervention (counseling)Promotion (chess)Gender and developmentGeographySocioeconomicsPolitical scienceSociologySocial changeEconomicsPsychologyEngineering

Abstract

fetched live from OpenAlex

The north-south movement in Ghana has taken a new dimension with females searching for better livelihood opportunities. The porters mostly end up living and working under deprived conditions and vulnerable to physical and reproductive health risks. Their expectations of making good money to enable them realise their dreams are hardly realised. Employing a mixed method of data collection, this paper examines the factors of this gendered migration and its socio-economic implications for the migrants in the CBD of Kumasi. It notes that the neo-classical economic theory of migration is inadequate in explaining the current pattern of north-south migration in Ghana. These female head porters face accommodation, health and work related risks/challenges at their destination. Yet deliberate policy intervention to stem the trend or address the associated challenges is lacking. It is the view of the paper that policy intervention must be geared towards bridging the socio-economic gap in development between the north and the south. The introduction of livelihood empowerment programs for these migrants at the destination will be a laudable thing but socio-economic development of the north, especially the promotion of female education will provide a viable long-term solution to the problem.

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)0.002
metaresearch head score (Gemma)0.001
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 score0.296
Threshold uncertainty score0.848

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.050
GPT teacher head0.359
Teacher spread0.309 · 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

Citations10
Published2015
Admission routes1
Has abstractyes

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