Hand-mapped stories of ‘Canadian’ Blackness, Failed Multiculturalism, and Black Humanity in a Predominantly White Mid-Sized City in South-Western Ontario
Bibliographic record
Abstract
The happy, upbeat narratives of multiculturalism in Canada misrepresent lived experiences of individuals who embody Canada’s narrative of multiculturalism and cultural diversity (Berry, 2013; Walcott & Abdillahi, 2019). This thesis asks young Black-‘Canadian’ adults to reflect on when and how they show up as their true, authentic selves while in their predominantly white mid-sized city (PWMC), Kitchener-Waterloo. Using art-based methodologies (Betancourt, 2015) and collective reflection (Mann & Walsh, 2013), I braided (Bancroft, 2018) the discussions to race and multiculturalism literature into five moments: Racist Experiences in Kitchener-Waterloo, Coping in Predominantly White (PW) spaces, Representation: Who needs it, Negotiation to Full Humanity and Community, and Encompassing All Peoples in Communities. In collaboration with the volunteers in this project, we call on those living in Kitchener-Waterloo to address the harms contributed to by racialisation and racism in tangible ways.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.033 | 0.012 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".