Bibliographic record
Abstract
This paper evaluates the economic benefits of self‐employment in Canada for 12 groups of ethno‐racial immigrants. It tests whether or not their self‐employment earnings are higher or lower than similar groups in wage and salary employment, whether ethnic minorities earn more or less from self‐employment compared to White immigrants, and whether self‐employment earnings of immigrant groups vary by their industrial sectors of employment. Using the Canadian Census 2006, I show that self‐employed ethno‐racial immigrants earn less than White immigrants. I also show that the economic benefits of self‐employment depend on the ethno‐racial groups and the industrial mix of their self‐employment. Cet article évalue les avantages économiques du travail indépendant au Canada pour 12 groupes d'immigrés ethnoraciaux. Il teste si les revenus du travail indépendant sont supérieurs ou inférieurs aux groupes similaires au sujet de salaire et de travail avec le salaire annuel, si les minorités ethniques gagnent plus ou moins de travail indépendant par rapport aux immigrés blancs, et si les revenus du travail indépendant des groupes immigrés varient par leurs secteurs industriels de travail. En utilisant le Recensement du Canada de 2006, je montre que les immigrés ethnoraciaux qui sont indépendants gagnent moins que les immigrés blancs. Je montre aussi que les avantages économiques dépendent des groupes ethnoraciaux et du mélange industriel de leur travail indépendant.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".