Re/making the 'Meeting Place' - Transforming Toronto's Public Spaces Through Creative Placemaking, Indigenous Story And Planning
Bibliographic record
Abstract
The theme of Toronto as a middle ground has been often referenced by historians and archaeologists alike: "geographically a meeting point between Canada's vast natural resource wilderness, such Atlantic Ocean seaports as New York and Montreal, and the sprawling continental Midwest, and since prehistory, a place of meditation and exchange between different cultures and peoples" (Carruthers, 2008, p.7). \n \nThe international community might know Toronto as one of the best cities in the world in liveability or quality of living (Mercer survey, 2016). Unfortunately, our city's important legacy as a middle ground or a "meeting point" has not been adequately celebrated both locally and internationally. The purpose of the research is to highlight the city's diverse culture and identity as a modern world city with a unique Indigenous heritage that goes back centuries, beyond the colonial era. Looking at history and its representation through the post-colonial lens, my research has the potential to not only build our unique sense of identity and pride as city's inhabitants, but to also serve as an important link in ongoing Canadian reconciliation efforts, in light of Truth and Reconciliation Commission (TRC) 2015 report and recommendations. \n \nI study how unearthing Toronto's forgotten/erased Indigenous historic narratives can remake our city a true "Meeting Place". I believe that by celebrating our pre-colonial history we have an opportunity to make Toronto more livable, more inclusive, more just city for all its Lefebvre's citadins. The research focuses on studying the city's Indigenous background, its current state of representation; on undertaking a comparative analysis of relative cases throughout the world; and on developing a local case study. Ideally, future steps will lead to establishing a centrally located art/history project and/or a network of small-scale public places where our Aboriginal history is showcased and celebrated. Toronto's story - our sense of place - will not be complete without acknowledging our Aboriginal roots. Beyond historical representation set in the past tense, it is imperative to talk in the present and even future language. Recognition of the continuous presence of Indigenous peoples on this territory is one of the building blocks in re-claiming the city by its Indigenous inhabitants. It is also an essential milestone in the process of Reconciliation.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.033 | 0.030 |
| Scholarly communication | 0.017 | 0.007 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 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".