Alternatives to Sprawl: Promoting infill development and brownfield redevelopment in Nanaimo, British Columbia
Bibliographic record
Abstract
Much has been written about both brownfield redevelopment and infill development as methods of improving the urban landscape. Barriers to these forms of urban and suburban development are all too often just superficially noted, and seldom subjected to critical analysis. Large metropolitan centres receive most mention; in fact, small, former industrial cities are rarely contemplated in the existing literature. To address shortcomings of critical analysis and the lack of attention on smaller cities, this study focuses on Nanaimo, British Columbia, a former coal mining and lumber processing community turned regional distribution and educational centre. The research is contextualized by a comprehensive review of the existing literature. Then, applying a qualitative research strategy, it was found through both a review of planning policies and in-depth interviews that Nanaimo was impacted differently than large metropolitan centres, and specifically in terms of the barriers that affect infill and brownfield redevelopment. As a result, Nanaimo suffers from additional economic challenges that render commonly-accepted strategies for encouraging infill and brownfield redevelopment less effective. Further, an examination of British Columbia’s program that was designed to support increased levels of brownfield redevelopment revealed that the program is essentially ineffective. Provincial funding models designed to induce redevelopment passively prioritized sites with little or no contamination, offering little financial aid to remediate seriously contaminated brownfield sites.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".