Spatial Justice Perceptions in High-Income and Low-Income Quarters of Tehran, Iran: Case Study of Niavaran and NematAbad Quarters
Bibliographic record
Abstract
Residents of northern Tehran are well paid and have better access to urban services, whereas their southern counterparts are paid less and deprived. Land value also differs significantly between the north and south. However, it seems that having more apt objective indicators does not necessarily lead to an enhancement in subjective indices. The hypothesis is that “Justice is not perceived at a higher level among the dwellers of the higher-income quarters of Tehran compared with their counterparts in the lower-income quarters.” To test this hypothesis, we attempted to define the criteria of justice, as perceived by Tehran citizens. According to previous studies, Tehran citizens generally perceive justice through four criteria: reduction of the gap between the poor and rich, government’s assistance to the poor, law enforcement, and absence of corruption. Based on these criteria, justice is evaluated in Niavaran (a high-income quarter in northern Tehran with good access to urban services) and NematAbad (a low-income southern quarter with poor services) via a questionnaire (a randomly selected sample of 200 people). The results show that despite the existing objective differences between the infrastructures, services, and incomes, there is no significant difference between these quarters in terms of the perceived (subjective) justice (based on Mann–Whitney U test). The citizens in both quarters are dissatisfied with the status of all criteria. It is concluded that only physical strategies and enhancement in city services have not been accountable for the promotion and a sense of justice. Thus, the attention of planners should shift from physical indicators to mental indicators.
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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 | 0.001 |
| 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 | 0.001 |
| 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".