The Geopolitics of Language and Literature Migration
Bibliographic record
Abstract
The notion of national languages, identifying a language with national unity, is a very modern idea, only about three centuries old and arising with the formation of modern nation-states. Before 1750, most people were bi- or multilingual, mixing whatever linguistic resources they needed in their lifeworlds. From 1780 to 1930 English speakers rocketed from twelve million to 200 million through language migration and settlements in Australasia, Canada, South Africa and the United States of America. The impact of colonial domination and empire through violence, exploitation, and resource extraction, and of the British industrial revolution from the eighteenth through the twentieth centuries, ensured that forms of transport (steamship, rail), communications (press, telegraph, telephone), science, and technology extended English’s reach as a global language. By the early twentieth century, American English emerged as the chief auxiliary language of world organizations (from the League of Nations to the UN) via massive investment in advertising, media, cinema, radio, tourism, Seaspeak and Police speak (international maritime and security communication networks), and the internet further extended the reach of specifically American English. The chapter traces the global circulation of English, driven by empire and neoliberal expansion, and its critique by decolonizing linguists, as contrasting views of English in the world, one instrumental and hegemonic, and the other more bottom up. I contrast colonial and neoliberal praxis with other models of civility, like hospitality, conviviality, decolonizing and devote the second half of the chapter to examples of black South African literature to illustrate the geopolitical afterlives of literary forms in translation/transnation.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.010 | 0.037 |
| Scholarly communication | 0.012 | 0.010 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".