Language planning and the British Empire
Bibliographic record
Abstract
This paper seeks to provide historical context for discussions of language planning in postcolonial societiesby focusing on policies which have influenced language in three former British colonies. If we measurebetween the convenient markersof John Cabot’s Newfoundland expedition of 1497 and the 1997 return of Hong Kong to Chinese sover-eignty, the British Empire spanned 500 years,2 and at its greatest extent in the 1920s covered a fifth of the world’s land surface. Together with the economic and military emergence of the United States in the 20th century, British colonialism3 is widely regarded as the main reason for the global role played by English today.4 It is also an indispensable element of debates about imperialism in general and linguistic imperi-alism in particular. Aims and Scope To discuss postcolonial language planning it is necessary to delve into policies which had a direct influence on language during colonisation. My main aims in this paper are to review the history of language policies in three areas of the Brit-
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".