Episodes of Non-employment among Immigrants to Canada from Developing Countries
Bibliographic record
Abstract
Using data from the Survey of Labour and Income Dynamics (SLID) we analyze non-employment episodes for immigrants from developing countries and compare their situation to that of immigrants from more developed countries and Canadian-born individuals between 1996 and 2006. The methods used allowed us to draw the following conclusion: significant differences exist between these three groups in the labour market mobility, the average duration of a non-employment episode, and the factors that affect the propensity to exit from a non-employment episode. These differences demonstrate a particular disadvantage for immigrants from developing countries. In fact, they tend to spend more time in non-employment episodes compared to their counterparts from the more developed countries and compared to Canadian-born individuals. À partir des données de l'Enquête sur la Dynamique du Travail et du Revenu (EDTR), nous analysons la situation des immigrants des pays en voie de développement au Canada en matière de non-emploi et leur situation par rapport aux immigrants des pays développés et personnes nées au Canada durant la période 1996-2006. Les méthodes utilisées nous ont permis de tirer la conclusion suivante : il existe des différences importantes entre les trois groupes au niveau de la mobilité dans le marché de l'emploi, la durée moyenne passée dans un épisode de non-emploi et les facteurs qui agissent sur la propension à sortir d'un épisode de non-emploi. Ces différences démontrent un désavantage particulier pour les immigrants originaires des pays en développement. En effet, ces derniers tendent à passer beaucoup plus de temps dans un épisode de non-emploi comparativement à leurs homologues immigrants issus des pays développés et les Canadiens de naissance.
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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 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".