Contesting a racialized regime of skill from the experience of recent immigrants: the case of Canada
Bibliographic record
Abstract
In the age of globalization when competition is fierce among nation states and individual employers and employees, skill has been promoted as a strategy to ensure high employability, productivity, and economic competitiveness. Critical scholars such as Fenwick (2006a, 2006b) and Sawchuk (2006) point out that the notion of 'skill' is far from consensual or accepted unproblematically. Fenwick highlights the inherent flaws of conventional conceptions of skill for their overemphasis on skill as a discrete competency and an individual acquisition through processes of mental reflection on concrete experience. Fenwick maintains that the skills-based initiative is based on the presumption that individuals are singular coherent beings ingesting rather than socially constructing knowledge and skills, which consequently ignores issues of collective learning and politics of knowledge. In particular, Fenwick questions the a-political approach for its failure to recognise the politics of skill by which particular knowledge and skill becomes valued and recognized. In this regard, Sawchuk (2006) joins Fenwick calling for a critical, integrated approach in understanding skill. Against this backdrop, this paper investigates the politics of skill in relation to the devaluation of immigrants' international credentials and prior work experience in Canada. Drawing on critical race theory, it examines processes of deskilling and reskilling and by extension contesting a racialized regime of skill in the age of transnational migration.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.062 | 0.024 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 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".