Bibliographic record
Abstract
Purpose This paper aims to examine perceived barriers and paths to success for Latin American immigrant professionals in the Canadian job market. Design/methodology/approach Findings are based on 20 semi‐structured interviews with Latin American graduates of Canadian MBA programs. Interviews were analyzed for emergent categories and common themes. Findings Despite their strong educational backgrounds, participants perceived several challenges to their success in the Canadian workplace, specifically, language barriers, lack of networks, cultural differences and discrimination. They also identified factors that influenced their professional success in Canada, such as homophilious networks and their Latin American background. Research limitations/implications By investigating stories of Latin American immigrant professionals, the study explores subjective views of immigration experiences and discrimination in this unique and rarely examined group. A larger sample will increase the confidence of the study's findings and future studies should examine dynamics of these issues over time. Originality/value This paper presents insight onto the labor market experiences and coping mechanisms of the currently understudied group of Latin American immigrant professionals in Canada. The study's qualitative approach enabled the examination of challenges experienced by immigrant professionals beyond those typically studied in this literature (e.g. devaluation of foreign credentials) and led to the finding that being Latin American can act both as a disadvantage in the form of discrimination and as an advantage as it differentiates immigrant professionals from other job seekers.
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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 | 0.006 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.012 | 0.011 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.018 | 0.001 |
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".