[Research progress on relationship between urban greenspace distribution and the socioeconomic characteristics of residents].
Bibliographic record
Abstract
Urban greenspace, which serves as a place for residents to connect with nature and relax, provides important ecosystem services. Access to greenspace is often related to the socio-economic characteristics of residents, which received a lot attention from researchers and practitioners. Previous studies have mostly focused on single city to analyze the spatial relationship between greenspace distribution and residents' characteristics. We conducted a meta-analysis with global studies. The objectives were to classify findings from different cases and investigate the impacts from the location of research area, indicator and analytical method, and summarized major factors influen-cing the relationship between greenspace distribution and residents' characteristics. The results showed that more than half of the cases (58.2%) found that the socially advantaged population benefited more from greenspace. About a quarter cases (25.4%) revealed the opposite, that was, the disadvantaged population benefited more from greenspace. The remaining case studies (16.4%) did not find significant correlation between them. The studies reviewed here were diverse in terms of scale, indicator selection, and analytical method. Overall, we found no connection between finding and the choice of scale/indicator/analytical method. The reviewed case studies were mostly conducted in cities of western countries, which differed in their development trajectories and urban characteristics from cities in China. To understand association between urban greenspace and residents' characteristics in China, we urged to carry out more local studies, which would potentially provide scientific evidence for building sustainable cities during rapid urbanization.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".