Agricultural Land Use Change and its Drivers in the Palestinian Landscape Under Political Instability, the Case of Tulkarm City
Bibliographic record
Abstract
Agricultural land-use change is unavoidable with population growth and economic development. This study investigated the causes and the consequences of agricultural land-use change in Tulkarm city in the West Bank of Palestine after the construction of the Separation Wall. With the aid of GIS data, the study found that urban and built-up areas increased by 54% during the period 1999–2009. About 80% of the new urbanization occurred on agricultural land. Further, the study presented views of the urban planners, decision makers and farmers in Tulkarm regarding the main factors affecting agricultural land-use change in the city using qualitative interviews. The study found that the political factors, especially the existence of the Wall and the division of land into areas A, B and C, have had a major impact on the city’s uncontrolled development and the diffusion of urban areas on the landscape around the city. At the same time, unprofessional planning, lack of experience, and lack of communication and coordination between different planning organizations are considered major factors leading to uncontrolled and unorganized expansion of the city. Other factors such as farmers’ socio-economic status, land fragmentation, and population growth play essential roles in selling-off agricultural land for urban uses. Studying the dynamics of agricultural land-use change and the factors that led to this change in the West Bank in general, and in Tulkarm in particular, might help shape more robust theoretical understandings of how factors of land change interact under different circumstances, including protracted conflicts.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".