An HR Perspective on Newcomer Work Experience: The Unlearning and Learning of Implementing Diversity, Equity and Inclusion in the Workforce in Canada
Bibliographic record
Abstract
Canada welcomes newcomers from around the world. The Canadian immigration system is led by Immigration Refugees Citizenship Canada (IRCC). IRCC has several mandates and by partnering with settlement agencies across the country they work to fulfill them. One of IRCC’s mandates is to integrate newcomers into the workforce. However, employment choices are limited, in part, due to the barriers employers experience in hiring newcomers. By discussing some of these barriers and their solutions we will look at how to implement best practices of diversity, equity and inclusion (DEI). Settlement agencies provide an important service helping newcomers integrate into Canadian society. Many times these settlement agencies hire newcomers to work for them. These workforces have a richly diverse work environment full of practical examples of how to implement DEI. Human Resource (HR) tools, resources and professionals are integral to hiring newcomers. From an HR perspective, newcomer work experience through attraction and recruitment provides clear practical examples of how to implement DEI hiring strategies. Newcomer work experience through Learning and Development (LD) provides examples of external stakeholder relationships and identifying DEI vendor procurement strategies. LD that occurs internally through peer-to-peer learning provides a welcoming work culture through cultural celebrations. Lastly, newcomer voluntary and involuntary departure provides opportunities to learn organizational gaps in doing the work of DEI. Therefore, we will discuss the newcomer experience in the workforce through a HR lens to reveal practical examples of how to learn and unlearn implementation strategies of DEI from employee attraction to departure.
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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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.064 | 0.022 |
| Scholarly communication | 0.019 | 0.005 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.005 | 0.010 |
| Insufficient payload (model declined to judge) | 0.013 | 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".