Navigating DEI: African Immigrants’ Experiences with Workplace Inclusivity Policies
Bibliographic record
Abstract
This paper explores the experiences of African immigrants in Canadian workplaces through the lens of Diversity, Equity, and Inclusion (DEI) frameworks. While Canada has long celebrated its multicultural identity, systemic barriers such as credential recognition challenges, implicit bias, and exclusionary practices persist. Drawing on literature about immigrants and African immigrants in North America, the paper highlights how DEI policies often overlook the nuanced needs of African professionals, particularly in Northern and remote regions. Recent backlash against DEI further threatens immigrant inclusion, undermining organizational commitments to equity. The paper identifies three major themes: exclusionary design of DEI policies, rigid implementation, and limited accessibility of supports. It concludes with an expanded research agenda addressing unfair discrimination, intersectionality, and the future of African immigrant integration in the Canadian labor market.
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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 | 0.008 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.052 | 0.020 |
| Scholarly communication | 0.011 | 0.005 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 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".