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Record W2993119935 · doi:10.7202/1096261ar

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

2023· article· en· W2993119935 on OpenAlexvenueno aff
Xiaomin Zhang, Haidee Kotze, Jing Fang

Bibliographic record

VenueMeta Journal des traducteurs · 2023
Typearticle
Languageen
FieldArts and Humanities
TopicTranslation Studies and Practices
Canadian institutionsnot available
Fundersnot available
KeywordsSalientLinguisticsPsychologyModalStyle (visual arts)Cognitive psychologySociologyLiteratureHistoryArtPhilosophyChemistry

Abstract

fetched live from OpenAlex

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.

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.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.360
Threshold uncertainty score0.298

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.084
GPT teacher head0.327
Teacher spread0.242 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2023
Admission routes1
Has abstractyes

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