‘Survival Employment’: Gender and Deskilling among African Immigrants in Canada
Bibliographic record
Abstract
Abstract Recent research points to a growing gap between immigrant and native‐born outcomes in the Canadian labour market at the same time as selection processes emphasize recruiting highly educated newcomers. Drawing on interviews with well‐educated men and women who migrated from countries in sub‐Saharan Africa, this paper explores the gendered processes that produce weak economic integration in Canada. Three‐quarters of research participants experienced downward occupational mobility, with the majority employed in low‐skilled, low‐wage, insecure forms of “survival employment”. In a gendered labour market, where common demands for “Canadian experience”, “Canadian credentials” and “Canadian accents” were uneven across different sectors of the labour market, women faced particular difficulties finding “survival employment”; in the long run, however, women’s greater investment in additional post‐secondary education within Canada placed them in a somewhat better position than men. The policy implications of this study are fourfold: first, we raise questions about the efficacy of Canadian immigration policies that prioritize the recruitment of well‐educated immigrants without addressing the multiple barriers that result in deskillling; second, we question government policies and settlement practices that undermine more equitable economic integration of immigrants; third, we address the importance of tackling the “everyday racism” that immigrants experience in the Canadian labour market; and finally, we suggest the need to re‐think narrowly defined notions of economic integration in light of the gendered nature of contemporary labour markets, and immigrants’ own definitions of what constitutes meaningful integration.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.020 | 0.005 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".