Stopping the wrecking ball: addressing demolition by neglect in Winnipeg, Manitoba
Bibliographic record
Abstract
Winnipeg’s built heritage is suffering from demolition by neglect, the lack of maintenance on designated heritage buildings which results in their demolition, often in the name of public safety. Although built heritage is a valuable community asset and the issue of demolition by neglect has been discussed for over two decades, little research has taken place to find solutions. This thesis explores the methods used for addressing demolition by neglect in Hamilton, Ontario; Ottawa, Ontario, and Edmonton, Alberta and seeks to understand if these methods would be effective in addressing the issue in Winnipeg, Manitoba. A document analysis and semi-structured interviews were used to uncover the methods for addressing demolition by neglect in the three cities while a focus group considered the applicability of the methods to Winnipeg. The result was a typology of strategies suggesting three recommendations for addressing demolition by neglect in Winnipeg, effective communication, supporting redevelopment and increased political will. When addressing a wicked problem like demolition by neglect, planners, policy makers, researchers and community groups need to take a customised, flexible approach that evaluates the individual context of each heritage building and work together to find solutions that will not only stop the neglect but support a vibrant and sustainable community.
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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 | 0.002 |
| Science and technology studies | 0.019 | 0.005 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".