Developing Effective Urban Open Spaces Policies; Using Excludability, Rivalry and Devolved Governance
Bibliographic record
Abstract
Urban open space provides both social and environmental services that range from 'private' to 'public' goods. This paper investigates the relationship between urban open space public and private goods and human wellbeing, to identify effective planning and management strategies based on theory and case studies of Ottawa and Canberra. The paper constructs a framework for effective management based on the economic principles of excludability, rivalry and devolved governance. This framework is the basis of an analysis of literature and exploration of unpublished surveys and reports on the gazetting and operation of open space networks in Ottawa and Canberra. Historically, gazetting urban open space provides 'public' ecosystem services (i.e. Non-excludable and non-rival) however, in operation; these open spaces offer a variety of services ranging from public to private goods. The findings indicate that urban open spaces are most effectively established by government. However in operation, they are more effectively managed collaboratively. By adopting the framework of excludability, rivalry and devolved governance, policy makers can better allocate resources for effective management of urban open space for human wellbeing.
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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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.016 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.002 | 0.007 |
| 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".