Cityscapes, Climate, and Mental Health: Designing Cities for Thermal Wellbeing
Bibliographic record
Abstract
The effects of the environment on human health have been a concern in society for centuries, and significant progress has been made in promoting public health by tackling environmental hazards. Similar to how sanitation and flood mitigation have become critical components of and indicators for urban life, we posit that urban heat poses a significant risk to human physical and mental health. Reflecting on origins of contemporary Western urban design, we see a significant amount of energy dedicated to addressing both physical and mental health through changes in urban design, ecosystems, and climate. Building from this, we advocate for a reframing of current issues in urban design that considers how urban climate affects our physical and mental health. This theoretical approach presents a fresh perspective on the intersection of design, climate, and mental well-being. It delves into the pathways that lead from elevated air temperature, exposure to sunlight, and interaction with natural environments to potential crises in mental health. We use urban climate as a lens through which we examine how urban design and mental health are connected and what solutions might exist to address previously identified urban design issues while also improving the mental health of communities.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".