Intermediary functions of landscape architects and non-profits in shaping green infrastructure within new residential developments in Ontario, Canada
Bibliographic record
Abstract
Socio-environmental issues caused by climate change and rapid urbanization require intermediary actors to bridge the gap between top-down development plans and bottom-up needs. In the context of planning residential green infrastructure (RGI), we explore the understudied functions of landscape architects (LAs) representing the “niche” designers of RGI, as well as environmental non-profits (NPs) who proactively work “between the boundaries” of public and private trust circles. We conducted semi-structured interviews with 16 participants to investigate their professional perspectives on promoting RGI in Ontario, Canada. The results identify perceived barriers, such as a lack of RGI-supportive municipal requirements and limited agency. To address these barriers, we propose a collaborative framework that integrates key and intermediary actors. Furthermore, we suggest that RGI benefit quantification tools can help empower intermediaries in building consensus among key actors to maintain and establish RGI. Our study introduces novel insights from the two intermediary groups, demonstrating their influence on RGI decision-making at different development stages, and discusses potential roles beyond the business-as-usual process of residential development. We suggest that synthesizing these understudied “intermediary” dynamics can help policymakers navigate potential socio-environmental shortcomings of streamlining housing development applications. Additionally, we anticipate that stakeholders in the residential development process can use the proposed framework to foster cooperative transdisciplinary relationships.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.016 | 0.007 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".