Diverse experiences of university education and entrepreneurship of native-born and immigrants in Canada
Bibliographic record
Abstract
Purpose To understand the relationship between studying abroad, receiving STEM education, completing master’s or doctoral education and the likelihood of becoming entrepreneurs among immigrant and native-born university graduates. Design/methodology/approach Statistical analysis of the 2016 Canadian Public Use Micro Data. Findings Despite the small differences between native-born and immigrant populations in the percentages of entrepreneurs, there are considerable differences in the location of study, STEM education and completion of master’s or doctoral education. Multivariate analysis suggests that since a higher percentage of immigrants are educated abroad, the significant difference in the percentage of each group who are entrepreneurs is narrowed, because education abroad is positively related to the likelihood of entrepreneurship. Originality/value We simultaneously compare the relationship between studying abroad, receiving STEM education and completing master’s or doctoral training and the likelihood of becoming entrepreneurs for immigrant and native-born university graduates.
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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".