Waqf Land in the West Bank and Investment Current State of Affairs
Bibliographic record
Abstract
This study has sought to find out the total area of waqf land in the West Bank, in terms of its types, and the efficiency of its investment. To these two ends, the researcher has tackled three major tracks: total area of waqf land in the West Bank; reality of its exploitation in the West Bank governorates; and efficiency of its investment. During his investigation, the researcher faced tremendous difficulties. On the theoretical side, and since the advent of the Palestinian National Authority, there has been a dearth of literature on these three tracks and on the investment of waqf land in the West Bank in particular. On the practical side, there was not a database available on this waqf land with the directorates of waqf. The researcher only managed to access some statistical publications and paper records. Therefore, he found himself compelled to process more than 2,800 files archived by the directorates of the waqf. He also conducted a number of personal interviews to identify the type of land and extent of its validity or suitability for investment. After collection of this data, he computed it and made an analysis of results according to type, geographical location, percentage of its occupancy using the analytical descriptive method. After data analysis, it was found that the West Bank was home to a treasure of waqf land but unfortunately it was not exploited well. The waqf land was found to be of two types. The first one is appropriate for all kinds of investment and it includes plain lands (92%), planted lands (4.1%) and land invested for real estate (1.2%). The second one is land that has been allocated for public benefit. This includes cemeteries, mosques and holy shrines (maqams). This type represents 2.7%. The results also showed that the percentage of plain land leased was very low. It did not exceed 1.6% of the total area of plain land in the West Bank. In contrast, the planted land amounted to more than 50%. This was due to the fact that the lease was used to being concluded seasonally. Undoubtedly, this poor exploitation of land has had negative effects on the revenues of waqf property and on the extent of its contribution to the Palestinian gross national product and maintenance of food security. The religious and historical shrines in the West Bank were found to be in very poor physical conditions, thus making the premises unsuitable for use. This poor physical condition of the shrines shows how much they have been neglected over the years. In the light of these findings, the study concluded with a number of recommendations, which, if taken seriously, could advance the waqf land sector. There is a necessity to make active use of unused waqf land and invest it anew in vital fields.
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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 | 0.000 |
| 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".