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Record W3004062726 · doi:10.21608/jes.2018.21063

EVALUATION OF SLUM AREAS DEVELOPMENT EXPERIMENT IN GREAT CAIRO REGION AND ITS ECONOMICAL AND ENVIRONMENTAL OUTCOME AN APPLIED STUDY ON MONSHAAT NASSER QUARTER

2018· article· en· W3004062726 on OpenAlexaboutno aff
Magda Ebeid, W. F.A Mohamed, Islam Shawky, E. S . H Zohny

Bibliographic record

VenueJournal of Environmental Science · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicUrban and Rural Development Challenges
Canadian institutionsnot available
Fundersnot available
KeywordsGeographySlumHuman settlementPopulationPovertyPopulation growthUrbanizationDeveloping countryQuarter (Canadian coin)Development economicsEconomic growthSocioeconomicsEconomicsDemographySociology

Abstract

fetched live from OpenAlex

The phenomenon of rampant growth of residential communities is a global phenomenon prevalent in most developing countries, 862.6 million people is the number of slum-dwellers are developing regions in the world, and is 60% of the population in Asia, while Africa maintain its two 26.2% and America including Latin America and Caribbean countries 13.1%، and the United Nations estimates for human settlements indicate that by the year 2030 is expected to live more than 50% of the world''s population in slums, which will be launched on our planet the planet of slums as well as that coming decades will witness the growth of urban unprecedented, particularly in developing regions and poor, which will lead to the emergence of a Further aspects of poverty in urban areas, and accompanied by many random areas, the emergence of lacking minimum decent living requirements and all types of services, including adequate housing, and it will be more obvious in the third world where accompanied by urban growth of any economic growth and social or take into account communities environmental dimension can be asserted. Slum housing is a phenomenon characterized by being spread out all over the Great Cairo region leading to deterioration in the housing environment in general and leaving negative impacts on the social aspects and human behavior that surround the region, in particular, and especially, the agricultural areas in the north, west, and south and even the desert area in the east. This random growth has got negative impacts on other Egyptian cities by transforming the agricultural lands randomly to urban districts and uses, increasing in turn the pressure on water networks, drainage, electricity and other utilities. This present research counts on the theoretical-analytical method for exposing and analyzing the different approaches of the schema set for each region to resolve the informal housing problem. The research also uses the case study approach, the (applied) part to improve performance. This research deals with policies and entrances dealing with non-formal housing in Egypt in general and the Greater Cairo region in particular, and evaluation of development experience, which has in the region particularly since the middle of last century trying to successive governments to find solutions to this important issue through the implementation of a number of policies to cope with rampant growth for those areas, and it has become serious since 2014 when committed the Egyptian Constitution, the successive elimination of slums and the citizen''s right to adequate housing and government research deals with assessing schemes during the period since 2000 to now to deal with informal areas and limit their growth, according to the scheme T and policies that have followed and to identify the pros and cons of those entrances and their contribution in solving the problems of informal areas and offer the experience of facility district development Nasser to evaluate the development of real estate and wealth provided and identify the elements of attraction and the elements of the package, which took place in the neighborhood to extract indicators and lessons planned lessons contribute to the formulation of a new planning vision for the development of the entrances to the housing areas of non-formal

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.256
Threshold uncertainty score0.425

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.083
GPT teacher head0.327
Teacher spread0.243 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations0
Published2018
Admission routes1
Has abstractyes

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