Socioeconomic inequities within and between cities in objectively measured green space qualities at small geographical scales: Evidence from Australia
Bibliographic record
Abstract
Abstract Objective To determine the extent of inequitable distributions in green space qualities in urban areas of Australia. Method Existing data from the cities of Sydney, Newcastle, and Wollongong in Australia was used to define green space qualities relating to accessibility, amenities/activities, beaches/coastline, biodiversity, incivilities, landcover and land use. Green space qualities were measured within multiple-scale network distance buffers for residential mesh blocks and linked with the Australian Bureau of Statistics Index of Relative Socio-economic Disadvantage (IRSD). Correlations were analysed using Spearman’s rank correlation coefficient between IRSD score (reversed; higher scores are more disadvantaged) and green space qualities aggregated over mesh blocks. Influence of IRSD, population density and random effects of population structures were examined using single-level and multilevel models. Spatial patterns and clusters were identified through choropleth maps and hot spot analyses. Results At the 1600m scale, more disadvantaged areas tended to have green spaces with lower percentages of nearby street trees to roads (Rho=-0.52, p≤0.001), lower percentages of slope >6° (Rho=-0.49), lower likelihood of threatened mammal species/habitat occurrences (Rho=-0.47), and lower percentages of tree canopy (Rho=-0.46). More disadvantaged areas tended to have green spaces with higher percentages of open grass (Rho=0.38, p≤0.001) and bare earth (Rho=0.33, p≤0.001) and higher densities of robberies (Rho=0.34, p≤0.001). For selected qualities, multilevel models tended to support the relationships that were found using Spearman’s rank correlation. Discussion Socioeconomic inequities in tree canopy, biodiversity and incivilities are present for green spaces in large and mid-sized Australian cities.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| 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".