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Record W4405888315 · doi:10.18280/ijsdp.191222

Resilient Schools Amid Epidemics: Multifunctional Design Strategies

2024· article· en· W4405888315 on OpenAlexvenueno aff
Hasan Y. Isawi, Khaled Al Omari, Wael Al-Azhari, Nisreen Azzam

Bibliographic record

VenueInternational Journal of Sustainable Development and Planning · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicDisaster Management and Resilience
Canadian institutionsnot available
Fundersnot available
KeywordsEnvironmental planningArchitectural engineeringCoronavirus disease 2019 (COVID-19)Environmental resource managementEngineeringGeographyEnvironmental scienceMedicineInfectious disease (medical specialty)

Abstract

fetched live from OpenAlex

This research aims to propose an epidemic-resilient school design through multifunctional space technique, as a promising solution that allows to guarantee social distancing, manages the circulation of students within classrooms, optimizes spaces and prevents significant disadvantages in terms of effectiveness, inclusion and flexibility.Furthermore; it allows to create more flexible, safe and efficient school environments, even in normal situations.The crucial role of a multifunctional space and its relevance in epidemic situations and normal conditions is highlighted and compared with alternative options such as movable or collapsible partitions.Multifunctional space has been shown to be advantageous in emergency management, allowing active reorganization of space without requiring outside intervention.The design of multipurpose spaces emerges as an effective and sustainable strategy to ensure safety and continuity in education.Investment in proactive design solutions proves crucial to foster continuous learning that is safe and adaptable to the changing needs of the educational environment.

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: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.004
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.000
Science and technology studies0.0010.002
Scholarly communication0.0020.002
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.033
GPT teacher head0.326
Teacher spread0.293 · 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 designTheoretical or conceptual
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
Published2024
Admission routes1
Has abstractyes

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