Immigrant effect in melting pot and multicultural societies: a comparison between the USA and Canada
Bibliographic record
Abstract
Purpose This study aims to investigate the role of socio‐economic institutions on immigrant effect (IE). The IE is to be empirically tested in two multi‐ethnic societies of the USA and Canada; comparing it in a melting pot and a multicultural approach. This effect is also separately to be examined in several provinces and states, each with its own social setting, in both countries. Design/methodology/approach The study examines data mainly collected from the census, immigration, and trade/export data in both countries, for the six‐year period of 2000‐2005. The paper compiles data in a panel data format on immigrant groups and trade with the country of origin of 27 (US) and 29 (Canada) immigrant groups. Findings The analysis implies findings almost the opposite of what was expected; immigrant effect exists in a melting pot and is not significant in a multicultural society. Research limitations/implications The study is limited to two societies, and still needs to be tested in other multicultural and melting pot countries across the globe. Practical/ implications While immigrants to Canada identify themselves more strongly with their new home than immigrants to the USA, Canada in general is not fully utilizing their potential in boosting foreign trade with the countries of origin of these immigrants. The paper also addresses some practical implications of the study for managers interested in better exploiting the benefits of immigrant effect. Originality/value Immigrant effect and its values in two very large immigrant recipient countries with very dissimilar social and institutional settings are systematically investigated. Based on the results of this investigation a number of implications for practitioners and policy makers is suggested.
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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 | 0.004 | 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) | 0.002 | 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".