The Transformation of Compact Rural Human Settlements in South Africa - The Case of Elim in Limpopo
Bibliographic record
Abstract
Land-use change entails changes in existing land use usually guided by spatial planning laws and the pollical influence of the state. The paper examined the impact of spatial strategies, laws, and policies on land-use change in Elim. A qualitative case study method was used to achieve the study’s purpose. Historical review of laws and satellite images of Elim in 1964, 1985, 1993, 2002, 2014, and 2020 were used to identify the impact of the rules and the extent of the land-use changes from 1964 to 2020. The study revealed that apartheid laws for creating exclusive homelands and townships for blacks significantly impacted Elim’s spatial pattern. The statutes produced displaced urbanization in homelands represented by large townships, betterment policy modernized traditional villages into compact villages with modernist characteristics, and Integrated Development Planning managed to integrate urban and rural areas to transform Elim into a mixed-use neighborhood. The analysis demonstrated that integrated and spatial development plans could guide development without zoning in rural areas, particularly in communal areas, to promote integration and create mixed-use neighborhoods. Modernist planning adopted a top-down approach, while the integrated development strategy embraces a bottom-up approach. The study will assist planners and lawmakers improve the spatial planning process.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".