Unveiling the Experiences of Racialized Immigrant Women in Cybersecurity - An Intersectional Qualitative Inquiry
Bibliographic record
Abstract
Skilled immigrant women’s integration in science, technology, engineering, and mathematics professions is influenced by the prevalent racial and gendered conditions present in these fields. This study employs qualitative interviews to investigate barriers to equity, diversity, and inclusion faced by immigrant women professionals in the cybersecurity sector in Canada. Using an intersectional approach, this paper unveils how racial and gender discourses affect immigrant women’s experiences of exclusion in the workplace. Findings suggest that immigrant women face multiple barriers at the intersection of gender, race, and immigration status to enter the sector and advance in their careers. Drawing on the interview data, this paper demonstrates how workplaces reproduce multiple forms of inequality for racialized immigrant women. These inequalities arise through the division of positions, the perpetuation of stereotypes that hinder upward mobility, work schedules designed for the ideal men employees, and the penalties associated with cultural differences that specifically disadvantage immigrants.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.007 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".