How Leadership Influences Urban Greenspace Provision: The Case of Surrey, Canada
Bibliographic record
Abstract
Much research has examined the socio-spatial distribution of, and access to, urban greenspace; the challenges of supplying greenspace, especially in periods of dynamic urban change, remain poorly understood. Multiple factors shape urban greenspace provision, however understanding the role of leadership as a factor remains somewhat elusive. Addressing this critical knowledge gap, we employed a case study approach, using qualitative methods, to hear first-hand from the key stakeholders involved with municipal urban greenspace provision, to investigate how different types of leadership affected greenspace provision in Surrey, Canada – a dynamically changing mid-size city. Semi-structured interviews with 32 purposively selected participants reveal that here, both leadership and organizational culture influenced resources and decision-making supporting greenspace provision. Aligned political leadership and organizational leadership witnessed a significant increase in Surrey's urban greenspaces – the converse occurred in a later administration. Findings provide insights into the governance of greenspace; especially how different types of leadership can play a pivotal role in effective greenspace provision.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.006 |
| Science and technology studies | 0.015 | 0.004 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".