Key factors for revitalising heritage buildings through adaptive reuse
Bibliographic record
Abstract
This study investigates the preservation through adaptive reuse of derelict heritage buildings at risk of demolition in urban settings in New Brunswick, Canada. Despite the demonstrated benefits of adaptive reuse in balancing heritage preservation and contemporary urban needs, small cities face significant challenges: financial constraints, regulatory barriers and technical limitations. Using a multiple-case study approach, adaptive reuse projects in Moncton, Fredericton and Saint John are examined to identify key factors contributing to their success. Findings reveal that prioritising structural adaptability, cultural value and long-term sustainability over profit-driven redevelopment models is essential. Successful adaptive reuse projects rely on collaborative governance frameworks, phased financial strategies, early involvement of technical expertise and active community engagement. This approach is critical to overcoming challenges such as hazardous material management, regulatory barriers and funding limitations. This study demonstrates that adaptive reuse can transform neglected heritage buildings into functional spaces, contributing to urban regeneration, cultural preservation and sustainability, while offering a framework for future adaptive reuse initiatives in similar contexts. Practice relevance The findings highlight key implications for advancing adaptive reuse as a strategy for heritage preservation and sustainability. Prioritising building location, adaptability and cultural value over profit-driven approaches is essential to fostering adaptive reuse initiatives. Establishing clear governance frameworks can align public, private and community efforts, facilitating collaboration to overcome common challenges. Financial incentives, such as grants or tax relief, can address issues such as hazardous material management, while adaptive regulatory processes can streamline approvals. Addressing expertise shortages through targeted training programmes and cross-regional collaboration is particularly important for smaller regions. Additionally, integrating sustainability principles and promoting material reuse within adaptive reuse projects can enhance environmental performance and urban resilience. These measures demonstrate how adaptive reuse can revitalise neglected heritage buildings into functional, purposeful spaces that contribute to cultural continuity, community identity and sustainable urban development.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.007 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 0.001 |
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".