Addressing Racial and Hate-Based Discrimination as Experienced by African Immigrants and Refugees in Waterloo Region, Canada
Bibliographic record
Abstract
Since the early 1990s, the demographics of Waterloo Region (Ontario, Canada) have changed dramatically. Early settlement patterns reflected “chain migrations” of Mennonite farm families moving to the region from Pennsylvania. Subsequent establishment of three urban clusters with German and British roots resulted in a predominantly White regional and Canadian population. A relaxation of Canadian immigration legislation, together with political unrest in many African countries, led to African immigrants and refugees choosing to settle in the Tri-Cities of Waterloo, Kitchener, and Cambridge. Canada has an international reputation as a welcoming country that celebrates diversity. However, the public view that multiculturalism is entrenched within Canadian society is not reflected in the experiences of African newcomers. New immigrants and refugees continue to face considerable problems and barriers in adjusting to their new country of residence. They are forced to deal with various manifestations of racism, including race-based hatred, as recurring patterns in their daily lives. Using firsthand accounts gathered over the course of two projects, this article explores the problems that African immigrants and refugees face in adjusting to life in Waterloo Region. Community activists and leaders are working together with police, the legal system, social service agencies, and schools to combat racism and hate-based discrimination. The article provides information on new African communities emerging in a predominantly White region. It discusses the challenges and the initiatives being taken by African community leaders and residents working together with institutions including the police, schools, municipal councils, social service providers, and community agencies to address racism and hate-based discrimination. The article also presents the general conclusion drawn by African community member participants within the two projects discussed herein: that “hate” is a pervasive problem, especially as experienced by African immigrant and refugee populations in Canada, and that the Criminal Code of Canada §§ 318 and 319 fail to address due to the restrictive definition and prosecution of hate crimes. The participants’ call for a redefined legal concept of “hate” and an accompanying revision of the Criminal Code seems to align with the aims and insights of critical or radical multiculturalism. These include the need to acknowledge, address, and identify ongoing issues of racial hatred and discrimination, in their multiple manifestations and as continuing structural problems within Canadian society.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".