Capitalism, immigration, language and literacy: Mapping a politicized reading of a policy assemblage
Bibliographic record
Abstract
Immigration for Australia and Canada is critical to sustain economic growth. Each country’s immigration policy stems from its vision of a nation that includes the role of language and literacy and a program of economic outcomes. While the authors acknowledge that economic integration through employment dominates immigration policies in Canada and Australia, the goal of this article is to critically examine and map how language and literacies in an immigration policy are positioned in relation to economic outcomes in neo-liberal times. Questions flowing from the article’s objective are: what does immigration produce, and what is its effect on how language and literacies are legitimated? The questions explore how capitalism decodes immigration, language and literacy, and in turn how immigration, language and literacies reterritorialize/reconfigure in the context of human and economic capital. These questions are taken up in an assemblage that includes Deleuze and Guattari’s writings on capitalism and deploys multiple literacies theory to read capitalism, immigration, language and literacy in the context of immigration policies prevailing in Australia and Canada. These two countries offer an interesting entry point for rhizomatic analysis since Canada’s government has, in recent years, been actively investigating Australia’s policies and their effectiveness in the successful integration of newcomers. Mapping a politicized reading of the immigration–language–literacy policy assemblage and questioning how this assemblage reconfigures is important as global migration intensifies around the world.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.017 | 0.047 |
| Scholarly communication | 0.018 | 0.010 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.007 |
| 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 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".