Capitalism, immigration, language and literacy: Mapping a politicized reading of a policy assemblage
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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 it