Local community involvement in the planning, design and development of previously developed land
Bibliographic record
Abstract
This research analyses the involvement of local communities in the design, development and planning of previously developed land (sometimes called ‘brownfield’). Specifically, it seeks to discover if such involvement improves or worsens the built form of previously developed land regeneration. A mixed method is employed that has involved the use of literature, case studies for the Maribyrnong River Valley, Melbourne, and comparable international case histories in the USA, Canada and the United Kingdom. The participation survey was conducted for the case studies using a ‘snowball sampling’ technique. Participants were selected from three broad groups- Residents (the ‘community of place’), planners and developers. Similar interviews were carried out for the international case histories. The findings are: 1. Intensive community collaboration is associated with higher levels of community satisfaction. 2. Community involvement can lead to both ‘good’ and ‘bad’ built outcomes. 3. The most consistent good outcomes are produced with early community involvement. 4. Community engagement that continues through to subsequent place making is beneficial. 5. Community engagement in urban design is more critical for the heavily used pedestrianised parts of a redevelopment. 6. Contemporary market conditions act against the effective creation of good urban places that the community want.
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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 | 0.006 | 0.008 |
| 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 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".