Strategic Collaborative Planning for Urban Liveability: A Comparative Review of Metropolitan Area Case Studies
Bibliographic record
Abstract
The issue of urban liveability has been extensively debated by global agencies and scholars, as evidenced by the New Urban Agenda, in response to the impact of urbanisation. Urban liveability pertains to the ability of urban communities to achieve a high-quality living environment. While participatory planning is often presented as a means of empowering communities to attain better living conditions, it has presented challenges for decision-makers. Furthermore, dominant actors often influence participation processes, benefiting certain community segments. As a result, collaborative planning has emerged as an approach that seeks to address the interests of multiple urban factors by promoting consensus in decision-making. This paper aims to examinethe collaborative planning methods that have been used in strategic planning that have empowered the community to participate in the planning process.Content analysis was employed as a research method to explore the empirical evidence of collaborative planning that has successfully empowered community participation in strategic planning and its impact on the liveability of metropolitan areas. Correspondingly, the Melbourne, Vancouver, and Helsinki case studies were selected based on five liveable city indices. This study's findings suggest a positive correlation between the implementation of collaborative planning by the selected metropolitan areas and their status as the most liveable cities. This paper contributes to the ongoing debate on the role of collaborative planning in promoting urban liveability.
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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 | 0.009 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.022 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".