Diversity management, inequality, and workplace productivity among immigrants: perception of black immigrant workers living in Canada
Bibliographic record
Abstract
Policies have opened Canada to migrants, often skilled immigrants on a permanent-resident basis, under programmes like Express Entry. Despite such policies, Black immigrants have faced several barriers in their workplace, such as low-wage pay and diminutive advancement opportunities. These findings are represented in a qualitative study that explores how Black immigrant workers in Calgary perceived diversity management and workplace inequality based on Johnson’s polarity management theory with the best applications of Benet’s Polarity of Democracy (POD) framework. The research used narrative inquiry to gather the subjective explanations of 10 Black immigrant workers who had spent five years in the workforce in Calgary, Alberta. The study exposes the polarity thinking of Canadian workforce diversity and equality. Participants viewed increased diversity as a solution to the problem of inequality, but significantly, many faced enduring challenges in achieving upward adjustments to equalise their workforce. Their polarity management approach revealed vast unconscious biases and ‘Canadian experience’ expectations still facing Black immigrants three decades after the multiculturalism policy took effect in 1988. The findings, which reveal the dilemma of Black immigrant workers, illustrate the possibilities of paradigm shifts from viewing diversity as an isolated problem to framing it as a polarity that needs to be balanced with an equally powerful one that is equality. A more balanced approach to diversity and equality shows a complete way forward, with positive social change including more equitable and just workplaces, agreement and a mutual understanding to propel industries and society toward a more just future.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.018 | 0.005 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".