Employment Barriers for Racialized Immigrants: A Review of Economic and Social Integration Support and Gaps in Edmonton, Alberta
Bibliographic record
Abstract
This article explores the strategies used by government-sponsored institutions dedicated to addressing systemic barriers to employment for racialized immigrants in Edmonton. The research involved conducting in-depth semi-structured interviews with service providers, employment program coordinators from different settlement and employment agencies, and a research and training centre operating in Edmonton, Alberta. The first objective is to understand the barriers racialized immigrants face through the hiring and promotion process. The second objective is to understand the support provided by those institutions and the impact of their equity policies on how they assist racialized Canadians in finding gainful employment. Lastly, this study explores the impact of the COVID-19 pandemic and the Black Lives Matter movement on the employment of racialized immigrants in Edmonton. The results show that around 50% of employment service providers acknowledged that visible minority immigrants face barriers while integrating into the labour market, including racial microaggressions in their jobs. In addition, the findings indicate a lack of programs tailored to the needs of racialized job seekers. Participants in this study reported that the Black Lives Matter movement raised awareness among employers regarding racial issues in the workplace. Hence, there is a demonstrated need for employers to undergo training to recognize and address racism in hiring, promoting, and retaining racialized employees at Canadian workplaces. Interviewees recognized that the COVID-19 pandemic negatively impacted racialized employees and newcomers. They recommended that Canadian companies establish educational programs that emphasize the importance and benefits of racial diversity, equity, and inclusion in the hiring process.
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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.014 | 0.017 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.003 | 0.002 |
| 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".