A better tool for designers and planners in neighbourhood design
Bibliographic record
Abstract
The quality of a neighbourhood is the degree to which it meets the needs of its residents. Many Canadian neighbourhoods are less than ideal quality due to environmental, transportation, health, safety, and social issues related to their physical form. An evaluative tool is needed for both design and post-construction evaluation of neighbourhoods in order to improve existing places and aid in the design of new neighbourhoods. The purpose of this study is to create and calibrate such a tool. Previous research has identified, from the literature, a list of physical features that relate to neighbourhood quality. Based on this research, a checklist of physical features has been created and applied in three qualitative case studies. For the purpose of comparison, three post-WWII (1950-1975) neighbourhoods from Kitchener and Guelph, Ontario, were selected to calibrate this neighbourhood evaluation instrument. It was demonstrated how data gathered from case study analyses using the evaluation instrument can be used to generate recommendations for neighbourhood design improvement. The evaluation instrument can also be used as a standardised case study method for the purposes of comparison, education, and research.
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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 | 0.047 | 0.107 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.017 | 0.023 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.034 | 0.012 |
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".