An Investigation on University Perception of Spatial Element Functionality of Irbid, Jordan
Bibliographic record
Abstract
Focus on well-being has gained prominence and is now an integral part of designing corridor spaces because of its profound effect on the campus environment.While numerous studies have investigated the characteristics of corridor spaces, the potential of transitional or corridor spaces remains underexplored.This study explored innovative applications of interior design to enhance visual appeal and functionality within enclosed internal corridors.Specifically, it focused on modifying spatial elements in the corridors of Jordan University of Science & Technology (JUST) in Jordan by introducing or removing architectural masses to create movement spaces with both practical and aesthetic value, evaluating the reality of the corridors space design.The research issue being addressed here revolves around the limited knowledge of how the design of corridor spaces affects the environment and user well-being criteria.As a result, there is a lack of established standards or guidelines for designing corridors to enhance spatial element functionality on perception.The paper employed theoretical approaches and survey questionnaires to gauge user satisfaction and assess their desire for change.Additionally, practical application methodologies were developed based on a novel design approach that emphasized the analysis of existing case study.ANOVA shows significant satisfaction differences among various aspects (p < 0.05).
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".