Systems Engineering Approach to Brownfield Redevelopment: Ralgreen Community Revitalisation in Kitchener, Canada
Bibliographic record
Abstract
A practical systems engineering approach emphasising strategic decision making in brownfield redevelopment is proposed and applied to a case study. The key to the approach is to apply strategic conflict analysis and then strategic project management in sequence. Strategic conflict analysis is carried out by using the Graph Model for Conflict Resolution, a decision support tool that helps decision makers (DMs) obtain insights into conflicts and locate possible resolutions, to investigate conflicts surrounding brownfield redevelopment. Strategic project management, which helps DMs find the best brownfield redevelopment solution, is implemented using multiple criteria decision analysis techniques to evaluate alternative courses of action according to a range of social, economic and environmental criteria. As a case study, the approach is applied to the Ralgreen residential community of Kitchener, Ontario, Canada, to demonstrate how brownfield redevelopment and community revitalisation are addressed in practice. Years before any residential construction at the Ralgreen site, a pond was infilled with organic wastes; the subsequent contamination produced serious problems for Ralgreen residents, including seepage, noxious odours, subsidence, potential health risks, and declining property values.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".