Self-appreciation and the Value of Employability: Integrating Un(der) employed Immigrants in Post-Fordist Canada
Bibliographic record
Abstract
The Canadian government actively recruits skilled immigrants1 who, by virtue of their human capital, are characterised as full of potential economic value. The widespread un(der)employment of skilled immigrants has consequently been problematised as costing the nation billions of dollars a year in potential economic growth and tax revenue (Toronto City Summit Alliance, 2003). Integration programmes that aim to address this loss, however, often simultaneously focus on immigrants’ ‘skills deficits’ and ‘lack of Canadian experience’, encouraging them to accumulate knowledge and skills in order to become more ‘employable’. This chapter examines the ways in which these programmes and immigrant un(der)employment have become key sites not only for cultivating entrepreneurial and investor subjectivities, but also for value-producing events. More specifically, I show how unemployment for skilled immigrants in Toronto, Canada, and inclusion into the nation require an investor ethos, that of investing in one’s human capital as assets. According to this financialised logic, it is more productive to invest in one’s future by self-appreciating in the present than it is to merely make an income in a low-paying ‘survival job’. Rather than surviving, one cultivates one’s human capital by investing in the self through potentially value-producing activities.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".