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Record W7128500386 · doi:10.64903/1480-6800-24.2.79

Impact of Mu’tah University, Jordan on Urban Expansion and Demographic Growth in the Adjacent Areas, 1985–2021

2021· article· W7128500386 on OpenAlexvenueno aff
Sattam Al Shogoor, Eman Almhadeen

Bibliographic record

VenueArab world geographer · 2021
Typearticle
Language
FieldAgricultural and Biological Sciences
TopicWater management and technologies
Canadian institutionsnot available
Fundersnot available
KeywordsUrban expansionPopulation growthPopulationEmigrationBuilt-up areaUrban areaUrban planning

Abstract

fetched live from OpenAlex

This study aims to analyze the history of urban expansion and demographic growth in the adjacent areas of Mutah University ( Jāmi‘atu Mu’tah ) in Jordan between 1985 and 2021, also shedding light on the impact of the university on this growth. A descriptive methodology was employed to chart urban expansion size, while an analytical method was used to analyze the air photos and create maps for the urban areas. GIS was utilized to analyze data through the maps and tables and follow the study area’s urban development. Results showed a significant increase in the size of population. That is due to the increase in population settled in areas surrounding the University, where the average gross total population growth between 1985 and 2005 was 6.95%, and the average natural increase was 3.9%. This means that the balance of emigration is positive and equal (3.05%), and that real growth was 7.63%. But the natural increase average was 2.9%; i.e., the balance of emigration was positive, amounting to 4.73%. The magnitude of the urban area was 2 077 km2 in 2005, which is a rate of urban spatial growth of 103% in the period 1985–2021, with annual urban growth averaging ca. 5.1%. The urban area expanded by some 3.21 km2 in the year 2021; between 2005 and 2021, annual urban growth averaged at ca. 3.5%.

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)0.000
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 score0.019
Threshold uncertainty score0.579

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.005
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.013
GPT teacher head0.203
Teacher spread0.190 · 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".

Quick stats

Citations0
Published2021
Admission routes1
Has abstractyes

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