Gender, International Training and Ethnic Visibility: An Intersectional Approach to Studying Engineers in Canada.
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».