Facing intolerance: Toronto black university students speak on race, racism and in(e)(i)quity
Bibliographic record
Abstract
This thesis examines the experiences of 12 young Black women and men in a hostile tertiary educational environment in Toronto. I critically analyze the impact of racism and racial discrimination on the everyday lives of the Black students, and the fact of schools and university campuses as seemingly uncontested sites for the reproduction of racist power relations and even outright hatred. This is an ethnographic study that utilizes an anti-racist, Black feminist perspective---the perspective of the "strong-willed resister" (Collins, 2000, p.98)---able to locate the racialised participants of this study at the centre of the research as "producers of knowledge and not merely consumer". What is palpable from the students' narratives, presented in their own words, is the need for ongoing critique and confrontation of racial oppression in Canadian society. The findings of this study provide clear evidence of the fact that despite the oppressive nature of Black life, the structured oppression that Black people face from early childhood in Canadian society, many still manage to succeed in the education system and become productive citizens contributing to overall societal development. Black women and men have managed and continue to manage to do intellectual work at all levels of society and many do this with a fierce commitment to "racial uplift". In fact, the desire to be engaged in self-reflection, self-improvement and community development work evidenced in the statements made by several of the young people interviewed for this study is commendable in view of the various economic and social oppressive forces they face in their own lives. The young people are empowered to form coalitions with others in and outside their particular communities. They confirm the need for support from academic institutions, governments, community agencies and families to make it possible that they learn their histories so as to gain the knowledge and language to contest the racist attacks that are fully part of the culture of their everyday and which irrefutably permeates every aspect of Canadian society.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.046 | 0.018 |
| Scholarly communication | 0.012 | 0.003 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 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".