Bibliographic record
Abstract
The use of English is commonly taken to be one of the distinctive features of globalization and Anglophone cultural hegemony. Is the appearance of the fictional translator in English writing an indication of deliberate resistance to “the global parochialism of Anglophone monoglossia” (Cronin 2003: 60)? Or are these imagined translators weak echoes of the same ‘questions of colonialism and cultural hegemony’ raised by Third World postcolonial plurilingual writers, writing in the language of the ex-colonizer? Are these characters the authors’ wistful attempts to construct a bilingual conscious-ness denied them through assimilation? These are the overarching questions that this paper will attempt to answer by looking at the fictional translator as a character and linguistic presence in writing in English through an examination of Michael Ondaatje ’s The English Patient (1992), David Malouf’s Remembering Babylon (1993), and Jonathan Safran Foer’s Everything is Illuminated (2002). The presence of the fictional translator in Canadian, Australian, and American writing in English suggests that the agent of discursive migration is operative even within regions of stubborn Anglophone monoglossia. If translators are agents of change, how do they operate in Anglophone writing in late modernity? And why is their presence so often awkward, unreliable, and even painful? I will argue that the fictional translator registers anglophone angst in spite of the lan guage ’s powerful global influence and publishing power.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.016 | 0.036 |
| Scholarly communication | 0.012 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".