Bibliographic record
Abstract
This paper presents an exploratory investigation of the experience of Latin American immigrants with Canadian MBA degrees in the Canadian job market. Based on a series of interviews, we explore and discuss challenges and strategies for success as perceived by these immigrants, addressing issues such as language, networks and discrimination. Throughout its history and to this day, Canada has been host to a very large population of immigrants. In fact, between 2005 and 2006, immigration accounted for two thirds of Canada’s population increase (Statistics Canada, 2006) and 18 % of Canada’s current residents were not born in this country (Statistics Canada, 2001). However, two years after arriving in Canada, the employment rate among working-age immigrants is only 63%, 18 percentage points below the national rate of 81% (Statistics Canada, 2005). Perhaps even more troublesome is the fact that of those immigrants that were in fact working, only 40 % had found a job in their intended occupation. Many of these immigrants arrive as skilled workers, a program designed to attract knowledge workers into Canada with the goal of driving growth of the country’s economy. This program establishes
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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 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.003 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.336 | 0.081 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".