Issues related to the integration of skilled immigrants in Quebec: A Latin American perspective
Bibliographic record
Abstract
This study explores the integration experiences of skilled Latin American immigrants related to their education and employment in Quebec (Canada) to understand how perceived systemic discrimination could influence their decisions towards remaining in the province. Scholarly sources indicate that immigrants from minority groups experience systemic barriers to their economic integration despite being more highly educated and qualified than native-born Canadians. Using Critical Race Theory, Postcolonialism and Global Neoliberal Capitalism as a framework, the study focuses on participants’ perception of situations of marginalization and exclusion in the labour market, their integration experiences as well as their suggestions towards the provision of fair opportunities to newcomers. Data were collected from face-to-face interviews with 10 participants living in Quebec, four participants living out of the province, and two leaders of the Latin American community in Montreal as well as from the photovoice method. Findings indicate that experiences of systemic marginalization faced when trying to access the labour market has led this group to rely on their own motivation and, to a lesser degree, on the networking of their own community members, rather than on government funded settlement services and programs offered to them, to get professionally integrated. Outmigration is likewise an alternative used to achieve their objectives. In addition, the study highlights the value interviewees give to their own cultural community while starting to question the provincial immigrant-friendly rhetoric of authorities. The study concludes with policy recommendations and suggestions for further research on the context unique to the skilled Latin American immigrant experience in Quebec
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.023 | 0.007 |
| Scholarly communication | 0.006 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".