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Record W4288033569 · doi:10.18280/ijsdp.170417

Barriers to Applying the Eco-System Resilience Approach as a Tool to Achieve a Sustainable Built Environment in Amman, Jordan

2022· article· en· W4288033569 on OpenAlexvenueno aff
Ahmad M. Alzouby, Esra’a W. Jebril

Bibliographic record

VenueInternational Journal of Sustainable Development and Planning · 2022
Typearticle
Languageen
FieldEngineering
TopicSustainable Building Design and Assessment
Canadian institutionsnot available
Fundersnot available
KeywordsResilience (materials science)Sustainable developmentBusinessSustainabilityEnvironmental planningEnvironmental resource managementProcess managementBuilt environmentKey (lock)Computer scienceArchitectural engineeringEngineeringCivil engineeringPolitical scienceGeographyEnvironmental scienceComputer security

Abstract

fetched live from OpenAlex

The eco-system approach is key to achieving a sustainable built environment. This approach Focuses on Making Cities Resilient with adapting (UR). Urban Resilience approach can play a role to achieve a sustainable built environment. The purpose of this article is examining barriers to implementing the UR concept towards sustainable development at the local level in Jordan. The focus group were studied; the Jordanian planners, architectural offices and stakeholders through a methodology based on semi-structured interviews and online questioner developed based on literature reviews data. The data analysis following a combined quantitative and qualitative approach. This research proposes that a viable sustainable ecosystem regulated with the resilience concept should be the framework adopted by environmentalists, decision-makers, and planners to facilitate and improve their sustainable future directions. From the results obtained were two types of challenges facing urban resilience, theoretically and practically with 12 challenge categories and 29 main barriers facing UR in Jordan were extracted. In addition to clarifying 7 principles that increase the effectiveness of UR.

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)0.002
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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.944
Threshold uncertainty score0.817

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.008
GPT teacher head0.229
Teacher spread0.220 · 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 designSimulation or modeling
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

Citations1
Published2022
Admission routes1
Has abstractyes

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