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Record W4399126056 · doi:10.18280/ijsdp.190507

Social and Economics Perspective of Rural-Urban Migration: A Case Study Middle and Lower Shabele Regions in Somalia

2024· article· en· W4399126056 on OpenAlexvenueno aff
Abdukadir Abdullahi Sheik Abdukadir, Abdullahi Ilyas Osman

Bibliographic record

VenueInternational Journal of Sustainable Development and Planning · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicAfrican history and culture studies
Canadian institutionsnot available
Fundersnot available
KeywordsEconomic geographyPerspective (graphical)GeographyRural areaDevelopment economicsRegional scienceEconomicsSocioeconomicsEconomic growthEconomyPolitical science

Abstract

fetched live from OpenAlex

The research is conducted in two major regions of Somalia: Lower and Middle Shabele in order to get a deep insight in the idea of socio-economic rural-urban migration patterns.People from rural areas prefer to move toward big cities under different socioeconomic factors.This research study revolves around the concept of rural-urban migration from the countryside of Somalia.It is briefly defining the concept of rural-urban migration.As urbanization is the process by which rural communities starts growing in an urban area or cities for the growth of expansion, for their standards of living, for excellent health facilities, for high education, jobs and several other social and economic reasons.The target areas from Lower and Middle Shabele are: Marka, Barawe, while from Afgoi Province the researcher will include Johar, Balcad, because these are the cities where people used to migrate immensely.The sample size will be 100 people from different areas.This research will be based on quantitative.Data is collected through questionaire.Results will be presented in tabulation and percentage form.The suggestions will be presented by the researcher according to the results of the data.

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)0.000
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: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.064
Threshold uncertainty score0.211

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.033
GPT teacher head0.308
Teacher spread0.275 · 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

Citations3
Published2024
Admission routes1
Has abstractyes

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