Indigenous identity and the urban environment : architecture for uncovering and restoring indigenous cultures in the City of Toronto
Bibliographic record
Abstract
Colonization has traumatized Indigenous peoples across Canada and the world for \ncenturies. Due to failure to recognize rights and traditional ways of life, many Indigenous \npeople are forced to relocate to urban areas in search of social and economic opportunities \nin hopes of a better future. However, the urban built environment currently does not \nsupport these urban Indigenous peoples where their identity is both physically and \nconceptually concealed behind colonial facades. Thus, even in an attempt to provide \nculturally specific space, the city is still guilty of inflicting trauma. This is especially true in \nthe city of Toronto where, despite having the largest Indigenous population in Ontario, \ncultural identity is virtually non-existent. \nAs such, the question being investigated is, how can Indigenous presence in the city of \nToronto be strengthened through restorative architecture that enhances cultural identity, \nnot hinder it? This thesis will explore the ways in which agency can be returned to \nCanadian Indigenous peoples through the creation of a Living Learning Centre; a hybrid \nprogram that supports the practice of Indigenous culture in the city while fostering the \neducation of non-Indigenous people in order to strengthen inter-cultural relationships. In \ndoing so, the objective is to generate greater discussion about issues faced by Indigenous \npeople, serving as a catalyst for change on a larger scale.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.027 | 0.026 |
| Scholarly communication | 0.010 | 0.002 |
| Open science | 0.002 | 0.009 |
| 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".