Measuring success: predictors of successful economic integration of resettled female refugees
Notice bibliographique
Résumé
There is a growing political, academic and practical interest in refugee integration in Canada. The challenge, however, is that not much of the existing research focuses specifically on refugee women and their unique experiences beyond mental and physical health. My dissertation contributes to addressing this gap by examining their successes and challenges in the Canadian labour market. Using the 2016 version of the Longitudinal Immigration Database (IMDB), this dissertation addresses the question: What are the characteristics that predict economic success among refugee women in Canada? A secondary question asks, to what extent does arriving during an economic recession influence the income of refugee women? This dissertation uses Critical Race theory, Intersectional theory and Segmented Labour Market theory informed by a quantitative research design to address these questions. These theoretical perspectives help to understand the findings suggesting that the barriers in the Canadian labour market help to sustain existing racism, discrimination and inequality refugee women experience. The findings indicate that the level of education at arrival for refugee women in Canada varies. Based on the sample population in my dataset and existing research, there are large numbers of refugee women with low levels of education compared to those with graduate-level education and of those with university degrees. A significant number (62%), however, have skilled and technical education and work experiences prior to their arrival to Canada. In addition, the most dominant skill level among refugee women in Canada is elemental labour followed by intermediate labour and clerical skills. Very few refugee women (mainly those aged between 35 to 49 years) arrive in Canada with managerial and professional skills. Education is an important predictor of the employment income of refugee women in Canada according to the results of the multivariate analysis. The findings in this study, not surprisingly, reveal that refugee women with university degrees earn significantly more than those with a high school diploma or less. As time in Canada and education levels increase, so does the chances of earning an income that is higher than median employment income. Arriving during a recession (2008) does not seem to have an influence on their wages in the long-term. In the short term, however, there is a decline in wages and income three and six years after arrival for groups who arrived during the latest recession.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,010 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,003 | 0,002 |
| Communication savante | 0,003 | 0,001 |
| Science ouverte | 0,001 | 0,003 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,005 | 0,001 |
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 source (Gemma direct ou Codex distillé), 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 ».