Green infrastructure planning in an urban context: "green plans" in four Winnipeg inner-city neighbourhoods
Bibliographic record
Abstract
This research project explores the integration of the concept of urban green infrastructure (GI) into three “green plans” developed by four Winnipeg inner-city neighbourhoods. Through a literature review, “green plans” evaluation, key-informant interviews, and a focus group interview, many factors that influence on the urban green infrastructure planning in Winnipeg have been identified. These factors were synthesized with a SWOT-TOWS framework to identify strategies and measures to address situations that these inner-city neighbourhoods might face in the process of urban GI planning. Several conclusions have been drawn to summarize the research results, including: green infrastructure planning in the Winnipeg urban neighbourhood context will be taking different physical forms in terms of network connection, which will have great impact on the GI benefits, GI planning principles and processes, and planning practices in those Winnipeg inner-city neighbourhoods; the “green plans” of the four Winnipeg inner-city neighbourhoods provide valuable lessons for preparing for future urban GI planning; and incorporating urban green infrastructure into current neighbourhood “green plans” will face various opportunities and challenges. Combined with some internal factors, these opportunities and challenges put GI planning in different situations, each of which needs their own strategies and measures.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 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".