Interface between buildings and the street: how building qualities and street qualities affect each other
Bibliographic record
Abstract
This Master's Degree Project (MDP) addresses the concept of "interface" in urban design practice, as it contributes to the design of urban streets and buildings, and to the quality of design in the public realm. In this MOP, the research focuses on the interface between buildings and the street. The relative theories of the interface space, including the area of the interface spaces, components and structures of the space and the functions of the interface, are discussed. All research work considers both building and street together, and combines urban design and architectural design. Two urban streets in different cultural contexts are critically analyzed to demonstrate how the interface of buildings and the street can affect the quality of the public realm. The two streets analyzed are 17th Avenue SW, Calgary, Alberta, Canada, and Tsuancheng Road, Jinan, Shandong, P. R. China. Through detailed analysis, the relative design language about interface patterns is developed to guide concept design options, and evaluations and recommendations are made. This MDP deals with issues in urban design practice, especially with buildings and urban streets. The concept of interface also contributes to the interrelationships among other urban components to improve the quality of public realm.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".