Hyper-conventional, unconventional, or “just right”? The interplay of normalisation and cross-linguistic influence in the use of modal particles in translated Chinese children’s literature
Bibliographic record
Abstract
The interplay between normalisation and cross-linguistic influence (CLI) has not been widely investigated in the specialised text type of children’s literature. Yet it may be proposed that normalisation would be particularly salient in translated children’s books as a consequence of the importance assigned to the needs of the target audience. This study embarks on an investigation of normalisation in Chinese children’s literature translated from English using modal particles as operationalisation. We first propose that a conceptual and empirical distinction needs to be drawn between normalisation and over-normalisation (or hyperconventionality), and that these are in tension with CLI. By combining quantitative and qualitative analysis, we then aim to shed light on whether translators tend to (over-)normalise children’s books to the norms of the genre in the recipient culture, or whether there is evidence of CLI effects that make the target texts more unconventional in this respect. Overall, the study finds evidence for normalisation, but not over-normalisation, with translated Chinese children’s books and non-translated Chinese children’s books largely similar in this respect. However, a small-scale qualitative analysis of two modal particles suggests that CLI and translators’ style play a role in some observable differences between translated and non-translated Chinese children’s books.
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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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.011 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".