Theoretical studies and design models of High Line parks: a systematic review
Bibliographic record
Abstract
High Line parks are an innovative landscape model that combines the adaptive reuse of abandoned infrastructure with ecological and environmental enhancement. This study explores the origins and development of High Line parks, focusing on the three phases of the High Line Park in New York, which has influenced similar projects worldwide. The research examines the defining characteristics of High Line parks and uses bibliometric analysis to identify trends in their development, including key themes, research subjects, and hotspots. It highlights the interdisciplinary contributions from landscape planning, design, management, and economics, emphasizing their role in shaping the High Line model. Additionally, the study proposes a framework for classifying design models based on the retention of original infrastructure, identifying four distinct design approaches. These findings offer valuable insights into the transformative potential of High Line parks for urban spaces. Future research should focus on three key directions: examining the role of community participation in mitigating green gentrification; modeling visitor perception and behavior using multimodal data; and assessing thermal comfort and health risks under compound environmental stressors, to support the inclusive and adaptive development of High Line parks in diverse urban contexts.
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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 | 0.021 | 0.087 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.021 | 0.021 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".