Bibliographic record
Abstract
In the last 30 years or so we have seen a proliferation of research projects on immigrants and non‐white minorities in the labour market (labour market segmentation) and as entrepreneurs (ethnic entrepreneurialism). Each of these literatures helps us understand the nature of immigrant and minority participation in the labour market, but each only offers a partial view. In this paper, I bring these topics together in an empirical investigation of the relationship between ethnic labour market segmentation and ethnic entrepreneurialism in Canada, using 1996 census data. I show that there is a close correspondence between the niches where immigrants and minorities find work, and those where they become entrepreneurs. Immigrants who are drawn to niches that offer few opportunities for self‐employment have low rates of entrepreneurship and, conversely, those who are over‐represented in niches with considerable scope for self‐employment are inclined to establish their own businesses. This shows that the propensity for self‐employment is, to an important degree, determined in the regular labour market. Therefore, entrepreneurship should not be seen as an intrinsically cultural phenomenon (i.e. that certain groups are “naturally” entrepreneurial), but instead as arising out of the opportunity structure associated with wage and salary labour.
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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 | 0.005 | 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) | 0.005 | 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".