Becoming an immigrant worker: Learning in everyday life
Bibliographic record
Abstract
This paper will examine the learning process of becoming an immigrant worker in Canada during current neo-liberal restructuring. We view learning as a complex process whereby individuals, embedded in social networks, develop a political analysis of their situation while they develop strategies of response. Our research is grounded in the work of our partner organization, the Immigrant Workers Centre (IWC), a support centre for workers. The paper reviews some of the political and economic forces that place immigrant workers at the bottom of the labour market in Canada. Study participants reflect these general trends. Our analysis draws on participants’ interviews and examines their experiences of leaving countries of origin, settling in Canada, and finding work. We conclude with a discussion linking these experiences with wider global forces. Résumé Cet article analysera le processus d’apprentissage d’un travailleur immigrant au Canada dans un contexte néo-libéral. Nous considérons l’apprentissage un processus complexe par lequel des individus, intégrés dans des réseaux sociaux, font une analyse politique de leur situation, en développant des stratégies de réponse. Notre recherche s’inscrit dans le travail de notre partenaire, Immigrant Workers Centre (IWC), un centre de soutien aux travailleurs immigrants. L’article passe en revue certaines forces économiques et politiques qui placent les travailleurs immigrants au bas du marché de l’emploi au Canada. Notre analyse fait état d’entrevues avec les clients du centre et passe en revue ce qu’ils ont vécu au moment de quitter leur pays d’origine, de s’installer au Canada et de trouver du travail. En conclusion, il présente une discussion faisant le lien entre ces expériences et des forces globales à plus large échelle.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".