Temporal Awareness in Urban Place: Al-Mutanabbi Street- Case Study
Bibliographic record
Abstract
Previous urban studies, which started from the twentieth century until now, have unanimously agreed to displace the human dimensions of the traditional city and replace them with inhuman dimensions in the modern city. Consequently, there were various calls for returning human dimensions to urban space via urban design theories. This research concentrated on the human temporal awareness as one of the dimensions that distinguish the human race and the connection of the connection between this dimension with urban place. The lack of urban principles resulting from human temporal awareness has created the knowledge gap. In addition, the objective of this research was to build an urban model with spatial indicators. In order to achieve this objective, a comprehensive theoretical framework was built by adopting a descriptive- analytical approach. The novelty of the research is to find an urban model with spatial indicators that works to enhance human temporal awareness and verify this model and give it a local character by applying it to urban space in Baghdad. The research faced significant limitations represented in the theoretical framework and the practical application of the research model. As for future research, the research recommends conducting studies to find a model with spatial indicators for the humanization of urban space, including all human dimensions.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
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".