Whose Heritage? A Critical Exploration of Heritage Conservation Practices in the City of Toronto
Bibliographic record
Abstract
This paper hopes to understand the underlying assumptions guiding heritage conservation in Toronto, and how or if these practices translate into a conservation system that favours the protection of certain heritages at the expense of others. Conservation practices in Toronto have evolved to fit within a value-based approach that recognizes expanded versions of heritage, moving away from ideas of only valuing physical and aesthetic aspects to understanding the cultural and social aspects of heritage. This study suggests that this evolution has been fuelled by heritage practitioners that move beyond the expectations of the Ontario Heritage Act to engage with communities and apply innovative strategies to conserve more diverse heritages. However, there remain barriers to meaningful community engagement in conservation processes, which results in a limited understanding of heritage and what is being valued. This study uses a mixed-method approach, including Geographic Information System mapping, a policy review, and interviews with heritage practitioners in Toronto. The findings of this study can be a starting point for further research into inclusive heritage conservation practices in Toronto, with a focus on advocating for a heritage conservation system that acknowledges and represents plural heritages.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.008 |
| Science and technology studies | 0.036 | 0.020 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".