Contested imaginaries: workfinding information practices of STEM-trained immigrant women in Canada
Bibliographic record
Abstract
Purpose This pan-Canadian study examines the information practices of STEM-trained immigrant women to Canada as they navigate workfinding and workplace integration. Our study focuses on a population of highly skilled immigrant women from across Canada and uses an information practice lens to examine their lived experiences of migration and labour market integration. As highly trained STEM professionals in pursuit of employment, our participants have specific needs and challenges, and as we explore these, we consider the intersection of their information practices with government policies, settlement services and the hiring practices of STEM employers. Design/methodology/approach We conducted a qualitative study using in-depth interviews with 74 immigrant women across 13 Canadian provinces and territories to understand the nature of their engagement with employment-seeking in STEM sectors. This article reports the findings related to the settlement and information experiences of the immigrant women as they navigate new information landscapes. Findings As immigrants, as women and as STEM professionals, the experiences of the 74 participants reflect both marginality and privilege. The reality of their intersectional identities is that these women may not be well-served by broader settlement resources targeting newcomers, but neither are the specific conventions of networking and job-seeking in the STEM sectors in Canada fully apparent or accessible to them. The findings also point to the broader systemic and contextual factors that participants have to navigate and that shape in a major way their workfinding journeys. Originality/value The findings of this pan-Canadian study have theoretical and practical implications for policy and research. Through interviews with these STEM professionals, we highlight the barriers and challenges of an under-studied category of migrants (the highly skilled and “desirable” type of immigrants). We provide a critical discussion of their settlement experiences and expose the idiosyncrasies of a system that claims to value skilled talent while structurally making it very difficult to deliver on its promises to recruit and retain highly qualified personnel. Our findings point to specific aspects of these skilled professionals’ experiences, as well as the broader systemic and contextual factors that shape their workfinding journey.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.029 | 0.014 |
| Scholarly communication | 0.010 | 0.002 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".