Exploring Indigenous newcomer relations in the City of Toronto: changing demographics, reconciliation and the capacity of city planning
Bibliographic record
Abstract
Toronto’s urban Indigenous and newcomer populations are rapidly increasing. As a result, the City is facing a new demographic reality that will change how the municipal government implements its plans, designs, and programs. Indigenous and newcomer groups often have similar lived experiences of marginalization and discrimination as equity-seeking groups and can work together towards a more just city. This Major Research Paper (MRP) lays at the intersection of this new demographic reality, the Truth and Reconciliation Commission of Canada’s (TRC) Calls to Action, and how planning might play an important role in supporting Indigenous-newcomer relations. A qualitative approach was used to explore the City’s current level of engagement with the TRC’s Calls to Action and the City’s Statement of Commitment to Aboriginal Communities in Toronto. Data collection included a document scan of City plans, engagement strategies, and divisional strategies; an initiative scan of events, reports to action, and council motions; and semi-structured interviews with City staff in the following divisions: City Planning; the Indigenous Affairs Office; the Newcomer Office; Economic Development and Culture; and Parks, Forestry, and Recreation. Key words: Reconciliation, Immigrant, Newcomer, Indigenous, City, Planning, Demographics, Toronto
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.034 | 0.024 |
| Scholarly communication | 0.011 | 0.004 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.001 | 0.003 |
| 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".