Analysis of Effective Factors on Presence of Citizens in Urban Spaces, Case Study: Towhid Square in Tehran
Bibliographic record
Abstract
This study attempts to respond the question that “which factors and indices are effective on citizen’s presence in urban spaces?” This is important because by identifying and analyzing this factors and indices, it could be possible to improve weaknesses and promoting strengths of each urban spaces. In Towhid Square, the inadequate space for citizens and the dominance of car traffic over pedestrians are some of the most reasons of lack of presence of citizens in the square as an urban space. The study is analytic and the data is collected from library and fieldwork. Cochran’s formula is applied to determine sample size which is 149. In the next step, by reviewing literature, indices were extracted. After providing questionnaire based on indices and by Likert Scale, we applied it to the case study and completed the survey. In data analysis, SPSS ®17 software is used and in the final step of analyses, four factors are achieved. The results of the study show, despite the hypotheses, the most important factor of non-presence (in contrast with passing) of citizens in Towhid square is “management” factor which is leading to the creation of other inhibiting elements of citizen’s presence in urban space.
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 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".