Urban Sprawl on Agricultural Land (Literature Survey of Causes, Effects, Relationship with Land Use Planning and Environment) A Case Study from Jordan (Shihan Municipality Areas)
Bibliographic record
Abstract
This paper adopts the problem of urban sprawl on agricultural land that has become a phenomenon affecting the national economy, the environment and the national food security. Firstly, urban sprawl will be defined then exploring the causes, effects, and its relationship with land use planning. It will point out to the Arabian and international experiences that have own policy in reducing this phenomenon, to take advantage from their experience and build on it to suit our own. Egyptian model was chosen to take advantage of scientific studies in the subject of urban sprawl and recommendations to reduce it, and the model of Canadian province of Ontario because their experience proven usefulness in reducing this problem in those areas. The paper sheds light on a local case in northern of Karak city, to present alive example of what is happening on the reality of agricultural lands in Jordan. The required analysis worked by using field survey, photographing, and aerial photographs from Google Earth, data and statistics collected from the Directorate of Agriculture in Karak and the municipality of Shihan. To explore the relationship between urban sprawl and environment, more analysis was done by (DPSIR) approach (Drivers, Pressures, State, Impacts, and Responses), which is based on the analysis of the relationship between cause and effect, linking development and human welfare to the environment. Keywords: urban sprawl, land use, agricultural land, Karak city, Shihan area, environment, DPSIR.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".