Bibliographic record
Abstract
While employers are playing an increasingly important role in immigration selection in Canada, little is known about how firm-level characteristics affect the economic integration of immigrants. Using a Canadian employer–employee matched dataset, this paper considers whether immigrants initially employed in low-paying firms in Canada experienced inferior earnings growth than those initially employed in high-paying firms. The results show that the large earnings differential observed between immigrants initially employed in low- and high-paying firms diminished only slightly over the subsequent 14 years, even when differences in demographic and general human capital characteristics are taken into account.Alors que les employeurs jouent un rôle de plus en plus important dans la sélection des immigrants qui s’établissent au Canada, on en sait peu sur la façon dont les caractéristiques au niveau de l’entreprise influencent l’intégration économique de ces derniers. Au moyen d’un ensemble de données appariées sur les employeurs et les employés, le présent document vise à déterminer si la croissance des gains des immigrants employés initialement au Canada par des entreprises à bas salaires est plus faible que celle des gains des immigrants employés au départ par des entreprises à hauts salaires. Les résultats montrent que l’écart important observé entre les gains des immigrants employés au départ par des entreprises à bas salaires et de ceux employés par des entreprises à hauts salaires ne diminuait que légèrement au cours des 14 années suivantes, même après avoir tenu compte des différences de caractéristiques démographiques et de caractéristiques générales du capital humain.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".