Too Old to Work?: The Influence of Retraining on Employment Status for Older Immigrants to Canada
Bibliographic record
Abstract
Research has established that immigrants who possess host-country human capital fare better on employment outcomes relative to those with foreign credentials. The purpose of this paper is to examine the effect of retraining on the employment status of older immigrants to Canada. Using the three waves of the Longitudinal Survey of Immigrants to Canada (LSIC), cross-sectional and longitudinal logistic regression models are conducted to test whether the probability of being employed is significantly related to retraining, while controlling for gender, age at arrival, immigration category, visible minority status, marital status, dependents in the household, highest level of education obtained outside of Canada, language proficiency, recognition of foreign work experience, health, and social networks. The results reveal that retraining at wave 2 is significantly related to the probability of being employed at wave 2. Longitudinally, a different pattern emerged in which the probability of being employed at wave 3 is greatest among those who did not pursue retraining at wave 1. Gender does not moderate the association between retraining and employment status for any of the waves. Selon la recherche, les immigrés qui ont acquis dans leur pays-hôte des qualifications en termes de capital humain s’en tirent mieux dans le domaine de l’emploi que ceux qui n’ont que des titres de compétence étrangers. Le but de cet article est d’examiner les effets d’une nouvelle formation sur la situation professionnelle de personnes d’un certain âge qui immigrent au Canada. À partir des trois vagues de collecte de l’Enquête longitudinale des immigrants au Canada (ELIC), nous avons réalisé trois modèles de régression logistiques transversaux et longitudinaux pour tester dans quelle mesure la probabilité de trouver un emploi est liée de manière significative à un recyclage professionnel, ceci en tenant compte du genre, de l’âge au moment de l’arrivée au Canada, de la catégorie d’immigrant, du statut de minorité visible, de la situation maritale, des personnes dépendantes de la famille, du niveau le plus élevé d’éducation atteint hors frontières, des compétences linguistiques, de la reconnaissance de l’expérience professionnelle étrangère, de la santé et des réseaux sociaux. Les résultats révèlent que, lors de la vague 2, ceux qui ont acquis une formation ont eu significativement plus de chances de trouver un emploi. Longitudinalement, la tendance qui apparaît est différente : dans ce cas, ceux qui n’ont pas tenté de se recycler lors de la première vague ont eu plus de possibilités d’embauche lors de la troisième. Quant à l’association entre formation complémentaire et la situation professionnelle, le genre ne joue de rôle dans aucune des trois vagues.
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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 | 0.001 |
| 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".