Skills to build the nation: The ideology of ‘Canadian experience’ and nationalism in global knowledge regime
Bibliographic record
Abstract
Skilled immigrant professionals are being aggressively recruited by once-exclusionary Western nation-states as crucial for their long-term national prosperity. Recent scholarship reads this as a rupturing of national identity and national membership due to the instrumental concerns of a global knowledge regime. In contrast, this paper argues that the welcome extended to skilled immigrants is provisional on their potential to secure nation-states’ interests in knowledge economies. Drawing on recent Canadian skilled labour policies, this paper shows how Canadian/Western experience is ideologically constructed as essential for immigrant professionals to succeed in the Canadian labour market. I argue that such a move enables the simultaneous functions of a ‘proactive state’, procuring necessary immigrant labour and a ‘defensive state’, shoring up the traditional, historically and culturally formed imagination of the nation. These contradictory functions of the state are anchored on a racialized discourse of skill, in which immigrants are typically cast as lacking, redeemable only through Canadian/Western education/training. I read this as a conditional welcome. Reinstating the contested figure of the Canadian as the desirable worker subject is how the nation form reasserts itself when identity-based nationalism is ideologically untenable and practically unsustainable. I thus contest the argument that national identity and national membership are decoupled in the context of the global race for skills, and instead welcome a dialogue between scholarships on skilled immigration and nationalism to facilitate better understanding of the enactment of nationalist ideologies in the site of the high skilled labour market.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.037 | 0.061 |
| Scholarly communication | 0.015 | 0.006 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.003 | 0.006 |
| 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".