Are planning methods culturally neutral? Examining how planners read multicultural landscapes
Bibliographic record
Abstract
Are traditional observational planning methods applicable to the growing range of contemporary urban settings? These methods, which include the approaches of Kevin Lynch, Jane Jacobs, William Whyte, Allan Jacobs, and Jan Gehl, were identified and analysed in an ethnocultural, suburban context. Specifically, they were applied to Pacific Mall in Markham, which was selected as a case study. Observations were compared to Dr. Zhixi Zhuang’s in-depth research, who determined what was and was not missed. This comparison determined that while the observational methods were able to read the landscape, there was cultural information not possible to establish through observation alone. Practicing planners need to better consider this cultural information when analysing space. This includes the culture of the space, the intended users of the space, legibility for these users, and what information to include in reports to better plan for places that are culturally unknown to the planners responsible for them.
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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 | 0.038 | 0.127 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 0.011 |
| Scholarly communication | 0.010 | 0.012 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".