Bibliographic record
Abstract
In the midst of rising urban density and its projected impacts on infrastructures and city dwellers alike, the importance of understanding the effects of built space on our biology and mental well-being is becoming essential to responsible spatial design. This thesis casts the designer as a practitioner focused on human impacts and synthesizes the lessons of current environmental psychology theory and mixed-methods biometric research into an analytical design framework to promote stress recovery and restoration. Architectural factors of scale, lighting, social organization, materiality, visual complexity, and enclosure are used as lenses through which to analyse an existing transit terminal and propose a reimagined space for waiting. \nAn identification of waiting rooms as high-opportunity areas within existing urban infrastructures led to the choice of Toronto Coach Terminal as a theoretical site in which to test the framework and to assess the value of restorative waiting environments. The subsequent design exploration takes the form of an open-ended kit of parts which creates seating areas and enclosures through a system of frames, connectors and panels to promote psycho-physiological restoration for a variety of users. \nTopics of framework development and user testing methodologies are addressed in an attempt to make disciplinary boundaries more porous and to co-solve questions with spatial implications using all available resources. Since both waiting and stress impact city dwellers more severely and the world is becoming increasingly urbanized, better access to and more seamless integration of restorative environments in the incidental spaces of the public realm would play a role in the management of stress at a population level.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".