The role of municipal development plans in the promotion of ‘Nature-First Urban Green Spaces’
Bibliographic record
Abstract
An opportunity exists to better integrate nature into our cities and towns. As humans we are hardwired to need nature and for most of us, this nature comes in the form of urban nature, or green spaces. Green spaces provide the opportunity for city dwellers to interact with and have a relationship with nature. Yet, reliance on the typical urban green space can no longer provide sufficient amounts of nature to foster the benefits which nature provides. The premise of this research is that changes in how green spaces are planned in suburban greenfield developments must occur to better protect and enhance the presence of nature in the built environment. In doing so, increased opportunities for urban dwellers to foster a relationship with nature are provided. In exploring the proposition labeled here as ‘nature-first urban green spaces’, the research methodology followed a qualitative case study of green space planning practices in Winnipeg, which included an analysis of municipal development plans and green space policies as well as informant interviews. Seven recommendations aim to enhance the presence of nature in Winnipeg and to increase opportunities for urban dwellers to foster a relationship with nature. The recommendations provide guidance to overcome existing green space planning challenges such as competing demand for land use and financial constraints, aim to strengthen the authority and effectiveness of green space policies, identify opportunities to further advance green space planning in Winnipeg, and encourage green space dialogue to promote nature-first urban green spaces. If applied, the seven recommendations can contribute to the planning and design of urban green spaces in Winnipeg to better reflect nature-first urban green spaces.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.010 | 0.009 |
| Scholarly communication | 0.012 | 0.004 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".