Governance and effectiveness in regional planning: an analysis of north american, european, and australasian practice
Bibliographic record
Abstract
Over the past several years, there has been resurgent interest in regional planning in North America, Europe and Australasia. Spurred by issues such as metropolitan growth, transportation infrastructure, environmental management and economic development, many states and metropolitan regions are undertaking new planning initiatives. These regional efforts have also raised significant question about governance structures, accountability and measures of effectiveness.n this paper, the authors conducted an international review of ten case studies from the United States, Canada, England, Belgium, New Zealand and Australia to explore several critical questions. Using qualitative data template, the research team reviewed plans, documents, web sites and published literature to address three questions. First, what are the governance arrangements for delivering regional planning? Second, what are the mechanisms linking regional plans with state plans (when relevant) and local plans? Third, what means and mechanisms do these regional plans use to evaluate and measure effectiveness? The case study analysis revealed several common themes. First, there is an increasing focus on goverance at the regional level, which is being driven by a range of trends, including regional spatial development initiatives in Europe, regional transportation issues in the US, and the growth of metropolitan regions generally. However, there is considerable variation in how regional governance arrangements are being played out. Similarly, there is a range of processes being used at the regional level to guide planning that range from broad ranging (thick) processes to narrow and limited (thin) approaches. Finally, evaluation and monitoring of regional planning efforts are compiling data on inputs, processes, outputs and outcomes. Although there is increased attention being paid to indicators and monitoring, most of it falls into outcome evaluations such as Agenda 21 or sustainability reporting. Based on our review we suggest there is a need for increased attention on input, process and output indicators and clearer linkages of these indicators in monitoring and evaluation frameworks. The focus on outcome indicators, such as sustainability indicators, creates feedback systems that are too long-term and remote for effective monitoring and feedback. Although we found some examples of where these kinds of monitoring frameworks are linked into a system of governance, there is a need for clearer conceptual development for both theory and practice.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".