Trends in Spatial Justice in Urban Space Planning: A Study of Selected Urban Spaces in 1st Kinda Neighborhood in the City of Kufa–Iraq
Bibliographic record
Abstract
The concept of spatial justice is an effective concept that researchers have begun to study to improve urban environments.All authorities seek to achieve this concept at the national, regional, local, and even urban levels.Based on this, the study aimed to derive effective indicators that can be applied to urban space planning, whether at the level of green spaces, open recreational areas….etc.Based on a study of the literature, a set of factors was identified: spatial security, community rights, diversity of activities, spatial empowerment, spatial resilience, accessibility, and social interaction.Several indicators were then derived, like the presence of cameras, participation, and fairness, in addition to the diversity of commercial, recreational, and cultural activities, the presence of training courses and community development programs, adaptability and spatial proximity, as well as the presence of benches, umbrellas, restaurants, and kiosks…etc.The study then focused on selected urban space in 1 st Kinda neighborhood in the city of Kufa, Iraq, where indicators were measured using Geographic Information Systems (GIS), field surveys, and questionnaire results using a fivepoint Likert scale, in addition to other statistical methods to quantitatively measure some indicators.The results indicated that the spatial security, spatial resilience, accessibility, and social interaction indicators achieved high levels of satisfaction, while the community rights indicators showed that the participation rate was low and unsatisfactory.However, the justice indicator achieved a high level of satisfaction, while the diversity results were very weak compared to the other indicators.
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 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 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".