BROWNFIELD REDEVELOPMENT IN LONDON, ONTARIO: DEVELOPING A METHODOLOGY FOR A BROWNFIELD DATABASE AND AN EXAMINATION OF POLICY INITIATIVES AIMED AT PROMOTING REDEVELOPMENT
Bibliographic record
Abstract
Brownfield redevelopment, because of its contributions to urban sustainability and environmental quality, has become a critical issue on cities* policy agendas and in urban development literature of late. There have been no studies examining the issue of brownfield redevelopment in London, Ontario, Canada nor have there been any studies that detail a method to create a city-wide inventory of brownfield sites. This research has three main objectives: to develop a GIS based methodology for creating a brownfield inventory; through interview analysis, indentify the barriers to successful brownfield redevelopment in London; and to determine whether or not the financial incentives in the London Brownfield CIP are effective mechanisms in promoting brownfield redevelopment. The major barriers to brownfield redevelopment in London are the public’s perception, high cost of redevelopment, liability, competition from greenfield sites, lack of demand and the complicated process of remediation. The effectiveness of financial incentives are based on local market conditions, that is, only when there is a demand for brownfield redevelopment will the incentives be utilized; the incentives do not create demand
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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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.015 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".