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Record W4378836625 · doi:10.18280/ijsdp.180508

Urban Redesign of Public Spaces in Residential Neighborhoods to Raise the Efficiency of Coexistence in the Event of Dealing with Epidemics Case Study - City of Aqaba (Jordan)

2023· article· en· W4378836625 on OpenAlexvenueno aff
Khaled Al Omari, Ghassan Suleiman, Hasan Y. Isawi, Firas M. Sharaf

Bibliographic record

VenueInternational Journal of Sustainable Development and Planning · 2023
Typearticle
Languageen
FieldMathematics
TopicCOVID-19 epidemiological studies
Canadian institutionsnot available
Fundersnot available
KeywordsEnvironmental planningGeographyEvent (particle physics)Public spaceRegional scienceCivil engineeringEnvironmental healthSocioeconomicsArchitectural engineeringEconomic geographyEnvironmental resource managementEnvironmental scienceSociologyEngineeringMedicine

Abstract

fetched live from OpenAlex

The epidemic following the spread of the coronavirus has led us to feel so distressed, as we have been forced to isolate ourselves -and in an instant we have lost the social relations between people.As the architect has the role of finding design solutions to ensure that in the event of an epidemic like the current coronavirus, people's lives don't have to undergo as many changes.This research has to study a block in an existing neighborhood in the city of Aqaba, Jordan, where it is shown for the lack of meeting spaces and entertainment spaces, people during the lockdown have suffered greatly.to prevent this from happening in the future, we have studied design solutions regarding free spaces in condominiums, to improve the social life of the inhabitants in the event of an epidemic.Naturally respecting the social distancing provided for by the laws of European countries and taking into account the recommendations of the World Health Organization (WHO).Interviews were made with the inhabitants of the neighborhood, a statistical and graphical analysis was carried out to find out the degree of acceptance of our design solutions.The result of the interview showed that 72.9% of the respondents strongly agree; 9.8% agree and 5.3% are neutral, while only 8.7% disagree and 3.3% disagree.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0030.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.204
GPT teacher head0.419
Teacher spread0.215 · 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 source (direct Gemma or distilled Codex), 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
Published2023
Admission routes1
Has abstractyes

Explore more

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