From Temporary Foreign Workers to Permanent Residents: Differences in Transition Rates Among Work Permit Categories
Bibliographic record
Abstract
Abstract: In recent years, temporary foreign workers (TFWs) have not only played an important role in filling Canada's labour shortages and advancing Canada's broad economic and cultural interests but are also seen as a large source of permanent residents admitted to Canada. TFWs come to Canada through various work permit categories under the Temporary Foreign Worker Program (TFWP) and the International Mobility Program (IMP). This research uses the 2019 Longitudinal Immigration Database (IMDB) to examine the factors that predict the transition rates of TFWs admitted to Canada between 2005 and 2014. The results of this study show that the five-year cumulative transition rates of TFWs are strongly associated with their work permit categories. Further, this study reveals that age, skill level, and initial destinations significantly affect the transition rates of TFWs in Canada. Finally, we discuss the implications of these findings for economic immigration policy and make suggestions for future research. Résumé: Ces dernières années, les travailleurs étrangers temporaires (TET) ont joué un rôle important non seulement en comblant les pénuries de main-d'oeuvre au Canada et en faisant progresser les intérêts économiques et culturels du pays, mais sont également considérés comme une grande source de résidents permanents admis au Canada. Les TET viennent au Canada grâce à diverses catégories de permis de travail dans le cadre du Programme des travailleurs étrangers temporaires (PTET) et du Programme de mobilité internationale (PMI). Cette recherche utilise la base de données longitudinales sur l'immigration de 2019 (IMDB) pour examiner les facteurs qui prédisent les taux de transition des TET qui sont admis au Canada entre 2005 et 2014. Les résultats de cette étude montrent que les taux de transition cumulative quinquennal des TET sont fortement associés à leurs catégories de permis de travail. De plus, cette étude révèle que l'âge, le niveau de compétence et les destinations initiales comportent le taux de transition des TET au Canada. Enfin, nous discutons des implications de ces résultats pour la politique d'immigration économique et nous faisons des suggestions pour des recherches futures.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| 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".