Land Allocation Policies of National Housing Programs and Projects: Proposals and Implementation Mechanisms for Housing Lands in New Cities in Egypt
Bibliographic record
Abstract
Egypt has been suffering from a significant housing problem of unbalanced housing provision for all segments of society. During the past decades, the state addressed this problem through successive land allocation and housing policies. However, those efforts were insufficient to resolve the issue, and there is still a gap between the demand and supply of adequate housing types for low-income groups. This paper, aims to come up with proposals Mechanisms for housing and land allocation policies in Egypt. The methodology of this research is based on monitoring and analyzing the development of land allocation and housing policies and programs from 1952 to 2020 to identify deficiencies in the different implementation mechanisms and their effectiveness. Also, three successful international experiences (Singapore, Australia and Morocco) representing various of housing land allocation policies are examined to identify essential lessons applicable in the Egyptian context. From the results of this analysis, and the outcome of structured interviews with Egyptian stakeholders in Housing field comprising housing academia and housing experts and planners. The paper finally proposes several institutional, legislative, planning, financial administrative and informational mechanisms to fulfill the land allocation policies and link them to the policies and programs of the national housing plans in Egypt.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".