The racialization of immigrants in Canada – a historical investigation how race still matters
Bibliographic record
Abstract
Purpose This paper aims to investigate the discursive ways in which racialization affects the integration process of immigrants in present-day Canada. By drawing on a historical analysis, this paper shows how race continues to be impacted by colonial principles implemented throughout the colonization process and during the formation stages of Canada as a nation. This paper contributes to management and organizational studies by shedding light on the taken-for-granted nature of discursive practices in organizations through problematizing contemporary societal and political engagements with “race”. Design/methodology/approach This paper draws on critical diversity studies as theoretical framework to problematize a one-dimensional approach to race and diversity. Further, it applies the Foucauldian historical method (Foucault, 1981) to trace the construction of “race” over time and to show its impact on present-day discursive practices. Findings Through a discursive review of Canada’s past, this paper shows how seemingly non-discriminatory race-related concepts and policies such as “visible minority” contribute to the marginalization of non-white individuals, racializing them. Multiculturalism and neoliberal globalization are identified as further mechanisms in such a racialization process. Originality/value This paper illustrates the importance of a historical contextualization to shed light on present workplace discrimination and challenges unproblematic approaches to workplace diversity.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.036 | 0.013 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".