Differentials in Metropolitanisation Trends in Lagos Peri-Urban Settlements
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".