Skilled immigrant women's career trajectories during the COVID-19 pandemic in Canada
Bibliographic record
Abstract
Purpose Despite immigrant-receiving countries' need for skilled professionals to meet labour demands, research suggests that many skilled migrants undergo deskilling, downward career mobility, underemployment, unemployment and talent waste, finding themselves in low-skilled occupations that are not commensurate to their education and experience. Skilled immigrant women face additional gendered disadvantages, including a disproportionate domestic burden, interrupted careers and gender segmentation in occupations and organizations. This study explores how the ongoing COVID-19 pandemic impacted skilled newcomer women's labour market outcomes and work experiences. Design/methodology/approach The authors draw on 50 in-depth questionnaires with skilled women to elaborate on their work experiences during the ongoing COVID-19 pandemic. Findings The pandemic pushed skilled immigrant women towards unemployment, lower-skilled or less stable employment. Most study participants had their career trajectory delayed, interrupted or reversed due to layoffs, decreased job opportunities and increased domestic burden. The pandemic's gendered nature and the reliance on work-from-home arrangements and online job search heightened immigrant women's challenges due to limited social support and increased family responsibilities. Originality/value This paper adds to the conversation of increased integration challenges under pandemic conditions by contextualizing the pre-pandemic literature on immigrant work integration to the pandemic environment. Also, this paper contributes a better understanding of the gender dynamics informing the COVID-19 socio-economic climate.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.012 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".