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Record W2766872079 · doi:10.5539/jsd.v10n6p14

Differentials in Metropolitanisation Trends in Lagos Peri-Urban Settlements

2017· article· en· W2766872079 on OpenAlexvenueno aff
Funmilayo Mokunfayo Adedire

Bibliographic record

VenueJournal of Sustainable Development · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicUrban and Rural Development Challenges
Canadian institutionsnot available
Fundersnot available
KeywordsHuman settlementGeographyMetropolitan areaSocioeconomicsDescriptive statisticsRegional scienceStatisticsArchaeology

Abstract

fetched live from OpenAlex

This paper examines the differential in the metropolitanisation of Lagos peri-urban settlements and the policy implication on locational quality of the emerging settlements. Two case studies of Ibeju-Lekki and Ikorodu were selected to represent the peri-urban settlements outside Lagos metropolitan regions. Using purposive sampling, thirty four settlements were selected which comprise sixteen and eighteen in Ibeju-Lekki and Ikorodu respectively. Data was sourced primarily through administration of 370 and 384 questionnaires to household heads in the selected settlements in Ibeju-Lekki and Ikorodu. Secondary data was sourced by conversion of analogue spatial images, the land use maps and satellite images of the study area to digital format. Spatial images from 1980 through 2016 were acquired for this study. Acquired satellite images from Google Earth archive were brought into ArcGIS environment for geo-referencing. Quantitative data was analysed using descriptive statistics while qualitative data was analysed using time series and satellite image analysis. Findings show a differential in transformation of the two cases due to varying demographic characteristics of residents, the locational convenience, level of linkages and the regional government housing policy. It is recommended that the regional planning should create a balance between the pace of development and infrastructural provision in the peri-urban to limit the disparity in development in Lagos peri-urban settlements.

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)0.000
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 score0.071
Threshold uncertainty score0.575

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.038
GPT teacher head0.327
Teacher spread0.289 · 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

Citations5
Published2017
Admission routes1
Has abstractyes

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