Urban greenspace to support social integration of immigrants? Case studies across Sweden
Bibliographic record
Abstract
The successful integration of immigrants into European societies has become a crucial policy issue in the past decade. Urban greenspace (UGS) provides social spaces for people with different ethnic backgrounds; however, the relationships between the social integration of immigrants with the reciving society and UGS have attracted relatively little research interest. This study aims to explore the role of UGS in enhancing the social integration of first-generation immigrants in Sweden (“new- Swedes”) by focusing on four forms of social integration: structural, interactive, cultural, and identificational. We draw on a sample of 280 interviews with new-Swedes from nine urban settlements in Sweden. Our results show that UGS in Sweden provides multiple opportunities for interactive integration among people from diverse cultures, including the receiving society, and that the accessibility, quality, and availability of UGS are crucial for structural integration. Although UGS do not primarily serve as venues for developing new relationships between new and native Swedes, they do facilitate social interactions within families and cultural communities. Additionally, UGS expose new Swedes to Swedish cultural norms regarding outdoor recreation. Our findings underscore the importance of critical infrastructure in promoting social interaction and integration. Active roles of immigrants in UGS planning and management will ensure that their needs and interests are considered in UGS design and offer important opportunities to be better connected to the receiving society. Finally, understanding the potential contribution of UGS also requires understanding the extent and depth of such integration.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.009 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".