Planning the resilient community: The case for using green infrastructure as a foundation
Bibliographic record
Abstract
My presentation will be a discussion on Green Infrastructure (GI) (i.e. the goods and services of nature) with my research premise being GI can serve as a foundational device for planning and building resilient, sustainable rural communities. The presentation advocates for a new land use planning system that is much more holistic in its consideration of nature as a central design piece for healthy and well-functioning communities. The presentation will fit within a 20 minute time slot and will provide a theoretical overview of the topic, as well as information on planning system case studies used here and around the world. To be more specific, my presentation will consist of the following elements: 1) definition of GI and why it is important;2) an overview of examples around the world where the use of nature and open space systems have formed the living infrastructural foundation for communities (e.g. Melbourne, Australia; Copenhagen, Denmark; Portland, Oregon), and also planning systems that highlight nature as a central design premise (e.g. UK’s 2012 National Planning Policy Framework); 3) case study work in Ontario demonstrating the effectiveness of GI. Best practice case examples from various municipalities in Ontario – both in the north and the settled south - will be highlighted. Information to be shared will include current research underway through the University of Guelph/Ontario Ministry of Agriculture, Food and Rural Affairs funding partnership.
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 | 0.009 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.031 | 0.057 |
| Scholarly communication | 0.011 | 0.009 |
| Open science | 0.003 | 0.016 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".