Mapping public access to greenspace & federal-municipal ownership in Canada’s Capital Region
Bibliographic record
Abstract
Urban greenspaces are widely studied and acknowledged as an important variable in creating livable cities with healthy populations. Yet in many cities, residents have inequitable access to greenspace. This paper examined the degree to which residents in Ottawa, Ontario, Canada have access to greenspace. Greenspace access is measured using Geographic Information Systems through a network analysis of greenspace service areas, and by considering population pressure, highlighting the distinction between different measures of accessibility. Greenspace access in the City of Ottawa is generally positive, however there are neighbourhoods that lack access to greenspaces. There are signals of inequities in access to greenspace, particularly related to income, education, ethnicity, and race, particularly on a neighbourhood-level basis. The federal government, primarily via the National Capital Commission, is pivotal in providing access to greenspace for Ottawa residents. Federal greenspaces are concentrated in the central area, along heritage rivers, and adjacent to the Greenbelt. This underscores the importance of collaboration and partnerships between the NCC and the City of Ottawa for the purposes of greenspace stewardship and planning. Additionally, the City of Ottawa should consider prioritizing its greenspace investment based on both social inequities and not having a greenspace within 800 metres.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| 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".