Learning how to "Skill" the Self: Citizenship and Immigrant Integration in Toronto, Canada
Bibliographic record
Abstract
The underemployment of foreign-trained professional immigrants became an intense focus of Canadian immigration policy and integration programs in the 2000s, particularly in Toronto, which receives more immigrants than any other Canadian city. This thesis examines how government conceived of this `skilled immigrant underemployment problem' and in turn promoted particular solutions to address it. Rather than viewing the role of government as needing to intervene in the labour market, it largely focused on reforming individual immigrants. In particular, integration programs tended to focus on "soft skills" training, which construed individual immigrants as skills deficient and as requiring training in "Canadian workplace culture". This dissertation thereby examines the ways in which immigrants were urged to sell the self, and how they were asked to become particular kinds of Canadian workers and citizens. I argue that these integration programs largely did not ameliorate un(der)employment, for they did not address the systemic discrimination new immigrants faced. Rather, I show how they increased the regulation of the un(der)employed and attempted to shape subjectivities in line with values dubbed "Canadian", which were integral to post-Fordist forms of labour and (neo)liberal rationalities of government. More specifically, I demonstrate how immaterial labour is deeply assimilatory. Rather than merely produce material products, workers must embody a brand/product, affectively and effectively, in ways that are deeply classed, racialized and gendered. These behavioural dispositions, however, were rendered technical and thus governable through a skills discourse. Additionally, I argue that these interventions reproduced a transition industry that facilitated and contributed to the cycling of new immigrants through endless job fairs and other training programs, a process through which they became flexible and entrepreneurial citizens who accepted responsibility for their own "employability". These programs thus constituted a means of rationalizing and managing (un)employment insecurity and of reproducing and regulating flexible labor.
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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 | 0.029 | 0.010 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| 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".