A Retrospective Look at the Social Construction of ‘Skilled’ Immigrant Workers in Ontario
Bibliographic record
Abstract
In this short chapter, I discuss how immigrant workers are objectified through the governing technology of public immigration policy. In particular, I comment on how ‘skilled immigrants’ in Ontario are turned into subjects of immigration policy and research through practices of social objectification and categorization (Foucault, 1972, 1977; Rabinow, 1984). I argue that such processes operated in a ‘discursive policy web’ constructed during the decade between 1996 and 2006. By pointing to some of the policy strategies employed, I attempt to ‘make visible’ the operation of power, which works through such techniques to govern social relations (cf. Foucault, 1977). To begin, I describe the scholarship behind the idea of a discursive policy web and the textual network that emerged in the period under analysis, which was chosen because it was through its discourses that the notion of ‘skilled immigrants’ was invoked as a policy solution to the skills shortage problem in the Province (see also Goldberg, 2006, 2007).
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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 | 0.001 | 0.000 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".