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Record W3033741338 · doi:10.5539/ells.v10n2p85

A Contrastive Analysis of Interpersonal Function Between the Chinese and English Versions of The Sight of Father’s Back

2020· article· en· W3033741338 on OpenAlexvenueno aff
Lijun Xin, Jun Gao

Bibliographic record

VenueEnglish Language and Literature Studies · 2020
Typearticle
Languageen
FieldPsychology
TopicLanguage, Metaphor, and Cognition
Canadian institutionsnot available
FundersNational Social Science Fund of China
KeywordsPolitenessModal verbSightModality (human–computer interaction)PsychologyLinguisticsInterrogativeValue (mathematics)ModalInterpersonal communicationInterpretation (philosophy)MoodContrastive analysisSocial psychologyComputer sciencePhilosophyArtificial intelligence

Abstract

fetched live from OpenAlex

As a reminiscent prose, The Sight of Father’s Back was written by the modern writer, Zhu Ziqing, in 1925. A wave of warm current floods a large body of readers since this essay describes, in earnest, love of father. This research performs a contrastive analysis of interpersonal function between the Chinese and English versions of The Sight of Father’s Back in terms of mood, modality, and evaluation meanings. We find that mood and evaluation meanings display parallel distribution. Declarative and exclamatory moods occur most frequently in both the Chinese and English versions, whereas interrogative mood is at a premium. Besides, various evaluative adjectives and adverbs are used in both versions. However, modality shows remarkable discrepancies. The English version tends to adopt modal verbs with median-and-low value, while most median-and-high value modal verbs are presented in the Chinese version. In our view, the exercise of median-and-high value modal verbs reflects the thoughts more directly. While the selection of median-and-low value modal verbs might be concerned with the need for politeness. Besides, diverse choices of modal verbs are incident to various modal meanings along with research purposes.

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)0.000
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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.184
Threshold uncertainty score0.261

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.011
GPT teacher head0.269
Teacher spread0.259 · 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 designQualitative
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

Citations0
Published2020
Admission routes1
Has abstractyes

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