Understanding Sense of community in Subiaco, Western Australia A Study of Human Behaviour and Movement Patterns
Bibliographic record
Abstract
Despite being an important physical environment capable of promoting social sustainability, sense of community and contributing to a better quality of life, residential streets and neighbourhoods have not attracted significant research interest until now. The integrated physical interconnected network of houses, front yards, walkways, alleyways and streets offers a high potential for community building through social interactions at a neighbourhood level. Understanding people’s movements, activities and perceptions about their streets can inform design practices and local planning policy in creating better communities. This study presents an investigation of a residential neighbourhood in Subiaco, Western Australia through the use of a mixed-method methodology based on observation and a perception survey. A total of 61 households were observed and interviewed during the spring and summer of 2016–2017 to develop useful typological models centred on activities, movements and resident perceptions. The findings endorse the importance of the residential street as a focus place for behaviour setting but argues that in the case of the Subiaco neighbourhood, which is part of a larger car-dependent metropolitain area, movement patterns– including vehicular, cycling, pedestrian modes and jaywalking, have no significant impact on social interactions. According to the perception survey, 82% of the Subiaco neighbourhood residents see activities across the street as generating the highest level of sense of community. The study expands both, the existing theory and approaches to urban planning, by emphasising the need for making neighbourhood streets the centre of liveability through better physical design which encourages and facilitates pedestrian movement.
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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 | 0.001 | 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 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".