Unraveling Socio-Ecological Inequities in Outer London: Cluster-Based Resilience Planning
Bibliographic record
Abstract
The sustainable development of cities urgently requires an understanding of the interaction between social equity and ecological quality, especially in the peri-urban areas that traditional environmental justice research has paid less attention to. Taking Outer London as an example in this study, the Comprehensive Social Equity Index (CSEI) and the Remote Sensing Ecological Index (RSEI) were constructed to explore the social–ecological coupling relationship and spatial heterogeneity. Four types of socio-ecological coupling were identified through the four-quadrant model, ordinary least squares (OLS), and multi-scale geographically weighted regression (MGWR). The results reveal the characteristics of nonlinear coupling: in addition to the dual disadvantages and advantages of society and ecology, there are also regional patterns where social conditions are advantageous, but ecology is degraded, and where society is weak, but ecology is rich. This indicates that there is a complex spatial dislocation relationship between society and ecology in the peri-urban. The research proposes a scale-sensitive governance strategy based on location, emphasizing the coordinated countermeasures of social reinvestment and ecological restoration, providing a new perspective for environmental justice and sustainable planning in the peri-urban areas of the UK.
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 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 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".