Bibliographic record
Abstract
Explanations for immigrant entrepreneurship commonly stress the blocked mobility thesis and the effect, which focus on the opportunities that the immigrant enclave offers to ethnic entrepreneurs. However, there are other types of attributes that enable the immigrants to find self-employment which can be revealed in the pattern of self-employment in different entry cohorts of immigrants to Canada between 1980 and 1995. The analysis focused in particular on whether immigrants with less human capital are more inclined to self-employment. The study was based on the Longitudinal Immigration Data Base, developed by Citizenship and Immigration Canada and Statistical Canada, covering data on 1.5 million immigrants, 69% of which are between the working ages of 20 and 64. Descriptive statistics showed that the immigrants' propensity to self-employment changes over time and by entry cohort. A logistic regression was used to predict the propensity of self-employment of immigrants in the 1995 tax year. The findings suggested that self-employment is often a source to supplement the immigrant's labor market income, and the possibility of self-employment increases with the number of years the immigrant had been in Canada. Moreover, male immigrants were more likely to become self-employed than their female counterparts. Those with higher human capital were also more inclined toward self-employment, as were immigrants with more resources and qualifications. Immigrants from Europe and North American showed a higher tendency to self-employment than immigrants from Asia, Africa, and Latin America.Whether they were attracted or driven to self-employment, Canadian immigrants with better qualifications and means were more likely to become self-employed. (CBS)
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.009 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".