The adaptive reuse of grain elevators into housing: how policy and perspectives affect the conversion process and impact downtown revitalization
Bibliographic record
Abstract
This study seeks to examine how the conversion of grain elevators into housing is an effective method of adaptive reuse. It uses theories and concepts on heritage preservation, downtown revitalization, place theory and environmental sustainability. Based on the literature review, there is a need for change in planning policy and there are both advantages and disadvantages to adaptive reuse. The methodology and data sources include examining and analyzing planning documents, surveys for the public and professionals, and demographic data. Case studies included converted grain elevators located in Australia and Norway and also a case study in Canada for the purpose of future recommendations. These methods answer the research question of how do planning policies and the perspectives of planning professionals and the public affect the process of the adaptive reuse of grain elevators into housing? Subsequent questions include topics such as whether adaptive reuse is an effective approach to downtown revitalization, which policies impede or facilitate the process, how perspectives influence decisions, and how demographics are linked to housing availability. The significance of this study on planning practice is that it helps form policy recommendations to address the needs of the public and help improve the efficiency of adaptive reuse in the planning process. In conclusion, the public and professionals were generally in favour of this type of adaptive reuse but many had concerns about cost and gentrification. Also, more policies need to be created that address adaptive reuse specifically. For the future use of the Toronto case study I recommended that converting the grain elevator into housing is the optimal choice. The limitations of this study include data availability, non-responses for surveys, language barriers, case study locations, and time constraints.
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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 | 0.007 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.010 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".