Under pressure: Factors shaping urban greenspace provision in a mid-sized city
Bibliographic record
Abstract
Urban greenspaces provide diverse ecosystem functions, services and benefits to residents. Much commentary has been offered to date about citizens' demands for more urban greenspace. Less attention, however, has been given to the 'supply side' pressures experienced by local government in delivering urban greenspace, particularly in mid-sized cities. Greater attention to factors shaping supply is warranted, especially in the context of rapid population growth. By understanding how existing greenspace provision approaches can stymie the efforts of local government to meet citizens' needs, new approaches can be identified. This paper assesses several factors shaping urban greenspace provision in Surrey - a city within the Greater Vancouver area. Insights are derived from in-depth interviews with key stakeholders, public documents, and census and municipal data about parks and their context as a specific type of greenspace. Our findings suggest that governance tools, economy and property markets, and financial and natural resources manifest as core factors influencing urban greenspace provision in Surrey. A reliance on governance tools premised upon standards has created park provision paradoxes. Treating greenspace provision as a largely technocratic exercise may be limiting Surrey's ability to respond to changing politics, economics and population trends. We point to alternative approaches.
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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 | 0.001 | 0.003 |
| 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 | 0.003 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".