Gender, International Training and Ethnic Visibility: An Intersectional Approach to Studying Engineers in Canada.
Bibliographic record
Abstract
Engineering in Canada has a large proportion of internationally-trained professionals. This reflects the rapid globalization of many professional fields in nations that have migration policies designed to attract the "best and brightest". Engineering is also a highly male-dominated field where women are tokens (below 15%) who may face a "chilly climate" as a result of their numeric underrepresentation and perceived occupational "inappropriateness". Empirical research that examines the transferability of immigrants' skills often highlights the risk of occupational mismatch or underemployment. Research on immigrant engineers' careers is usually restricted to studying men, and the career prospects of immigrant women engineers are understudied. This dissertation aims to address this gap by using intersectionality as a framework to examine immigrant women's combined vulnerabilities as internationally-trained professionals re-establishing their careers in a new country and as female tokens in a male-dominated field. Drawing on the nationally-representative 2006 Canadian census data, a series of multinomial logistic regressions are carried out to predict the likelihood of individuals with engineering training being successful in gaining entrance to: (1) the Canadian labour market; (2) the field of engineering; and (3) advanced positions within the engineering field. The intersection of gender, origin of training and ethnic visibility is examined by modeling the combined interacting effects of these three status variables. The results demonstrate that gender and, immigration and visible minority statuses work as independent and intersecting forces. Women, immigrants and visible minorities, each, are at a disadvantage in obtaining these different career outcomes. The intersections between the statuses create complex and diverse trajectories of disadvantage showing that the experiences of immigrant women engineers cannot be understood by studying immigrant men or women as a homogeneous group. Specifically, immigrant women are at a cumulative disadvantage in their chances of obtaining any employment, employment in engineering, and securing advanced positions within the field. Moreover, the analysis suggests that the cumulative disadvantage of immigrant and visible minority female engineers is produced by different forces. The results of this study highlight the relevance of the intersectionality framework in studying immigrant women professionals and offers important methodological considerations in studying occupational match versus mismatch.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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; a candidate call from one teacher head, 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".