Investigating the Knowledge interface between Stakeholder Engagement and Plan-Making
Bibliographic record
Abstract
The ‘ideal deliberative procedure’ provides structure to the process of stakeholder deliberation, yet creates a tension with the formal processes of strategic plan-making. This paper examines process design by drawing upon communicative planning theory, and the rational comprehensive model and deliberative democracy literature. In the context of metropolitan strategic spatial plan-making, the aim of this paper is to examine how the knowledge interface between the process of stakeholder engagement and the process of plan-making enables or inhibits implementation of the plan. A retrospective study examining the development of two metropolitan strategic spatial plans: Greater Perth's the Network City plan and Greater Vancouver's the Livable Region Strategic Plan is provided. It is revealed that the engagement of the planners, the public and the politicians occurs within formal stakeholder engagement ‘events’ positioned at different stages of the plan-making process. This paper reveals that the deliberation among the professional planners and the politicians at the process design stage steers the plan-making process in a manner that retains its legitimacy and creates a more implementable plan.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.021 | 0.054 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.018 |
| Scholarly communication | 0.011 | 0.013 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".