Bibliographic record
Abstract
This book critically examines the public participation processes in urban planning and development by evaluating the operations of Planning Advisory Committees (PACs) through two meta-criteria of fairness and effectiveness. Traditional models of public participation in planning have long been criticized for separating planners from the public. This book proposes a novel conceptual model to address the gaps in existing practices in order to encourage greater public involvement in planning decisions and policymaking. It assesses the application of the evaluative framework for PACs as a new approach to public participation evaluation in urban planning. With a case study focused on the PACs in Inner City area of Canberra, Australia, the book offers a conceptual framework for evaluating fairness and effectiveness of the public participation processes that can also be extended to other countries such as the United States, the United Kingdom, New Zealand, Canada, Scandinavian countries, the European Union, and some Asian countries such as India. Offering valuable insights on how operational processes of PACs can be re-configured, this book will be a useful guide for students and academics of planning and public policy analysis, as well as the planning professionals in both developed and developing countries.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".