Spatial distribution and equity of urban green space provision in Tehran Metropolis using hybrid Factor Analysis and Analytic Network Process (F′ANP) model
Why this work is in the frame
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Bibliographic record
Abstract
This study examines the spatial distribution of urban green space (UGS) provision in Tehran Metropolis and how equitable is its distribution among the city’s neighborhoods. Geographic Information System (GIS) and an augmented Factor Analysis and Analytic Network Process (F′ANP) method is utilized to extract the underlying dimensions of GS provision, determine the weights of their composing indicators, and compute the composite GS provision index (CGSI). Jenks Natural Breaks classification, Gini index, and auto correlation analysis methods are applied to examine the spatial distribution and inequity of UGS provision components. The results indicate that in Tehran metropolis, (1) UGS provision is composed of three UGS constructs: Accessibility, quality and quantity, (2) UGS constructs in the city’s neighborhoods are clustered, (3) the inequity in the distribution of UGS quantity is very high, (4) and contrary to general perceptions, the inequity in UGS accessibility in neighborhoods with low socio-economic status is very low. The findings of the study can be beneficial for local urban green space planners since it is applied at the neighborhoods level and the applied methodology could be replicated in similar studies. • Environmental justice provides a useful framework for Urban Green Space (UGS) provision assessment. • The study shows how F′ANP model could be utilized for composite UGS provision index construction purposes. • The results of this study shows that UGS access inequity in Tehran city’s neighborhoods with low socio-economic status is very low.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it