The Future of Healthy Living: \nImproving the impact of urban living environments on residents
Bibliographic record
Abstract
Healthy living is always a significant part of community well-being. The quality of human living spaces plays an important role in promoting health and wellbeing. Increasingly, there are many mental and physical health concerns in multicultural, complex urban living environments. Living environments, which include dwellings and community areas, become important examples when considering potential leveraging points to improve the quality of healthy urban living. Considering the current rapid technological development and cultural shifts, there are many significant changes in people’s lifestyles that will affect their living space needs. This project begins by describing the current development of residential living space in urban cities, such as Toronto, and analyzing the dynamics at play and their patterns of social support. Utilizing foresight methods, systemic analysis, and generative research methodology, this project will research urban ecologies to understand the relationship between humans and their living spaces and how space could shape their wellbeing, highlighting the physical and mental issues that impact people today in urban city living spaces. Through an investigation of various aspects of current urban development patterns, possible future directions and solutions for the living environment may be proposed. This research will focus on intervention strategies and key design principles for future residential design innovations as a guide, catalyst, and opportunity for future residential design inspiration.
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 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.021 | 0.002 |
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".