New Immigrants’ Perceptions of Ethnic Small Businesses
Bibliographic record
Abstract
Recent immigrants in pro-immigration countries face the burden of economic instability due to lack of host country work experience, resulting in job search in the unskilled labor market as cashiers, grocery packers or waiters. At the same time, many other immigrants have taken on entrepreneurship by starting businesses that reflect deep linkages to their mother country (nationalism) in terms of products, network and cultural work environment, establishing a source of unskilled employment for other immigrants. However, there are both practical and theoretical needs to understand intergroup phenomena occurring when an immigrant intends to apply for work in a business of different ethnicity. This paper explores job seekers’ perceptions of nationalistic ethnic businesses concerning intergroup feelings (prejudice and group competition), social identity issues (nationalism) and perceived job opportunities. Results on an experiment of immigrants in Montreal indicated that, consistent with the main hypothesis, nationalistic businesses are deemed more prejudiced by job seekers than non-nationalistic ones. Interestingly, the evidence also suggested that job seekers regard each business ethnicity differently and that those prior ethnic impressions affected levels of expected prejudice. Moreover, individual variables such as job seeker’s openness to experience and ethnic identification revealed marginally significant impact on perceived prejudice. This work helps enlighten literature in intergroup conflict in the context of job search and ethnic business and enhance literature on immigrant experience. Important practical implications for recent immigrants, ethnic businesses and immigration policies are discussed in favor of reducing perceived prejudice in multicultural countries.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 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".