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Record W2981045100 · doi:10.5539/ijel.v9n6p64

English Compliments by Chinese and German Female EFL Speakers

2019· article· en· W2981045100 on OpenAlexvenueno aff
Ruoyu Zhu

Bibliographic record

VenueInternational Journal of English Linguistics · 2019
Typearticle
Languageen
FieldArts and Humanities
TopicLanguage, Discourse, Communication Strategies
Canadian institutionsnot available
Fundersnot available
KeywordsGermanPragmaticsPsychologyLinguisticsFactor (programming language)Social psychologyComputer science

Abstract

fetched live from OpenAlex

Based in the field of variational pragmatics, the present study investigates the effect of one of the macro-social factors, regional factor, on the use of compliment strategies. More specifically, the present study would like to find out whether in the same situation, the compliment strategies used by Chinese female EFL speakers and German female EFL speakers differ. A questionnaire was designed in order to collect the date, which employs two Discourse Completion Tasks (DCT). A corpus of 20 dialogues were collected, consisting of 10 from Chinese informants and 10 from German informants. In the present study, all social factors, except the regional one, are controlled, which means the informants were asked to complete two dialogues which happen in two given situations, between two friends in the same age group and of the same gender (sex). Furthermore, the gender (sex) and age of the informants are homogenized, which ultimately makes the regional factor the only prominent and researched macro-social factor. After analyzing the date, the results of each of the two given situations are respectively achieved. First, by comparing the frequencies of the two main types of compliments, explicit compliments and implicit compliments, used by Chinese female EFL speakers and German female EFL speakers. Second, by comparing the frequencies of varieties of sub-categories of the two main types of compliments. Moreover, the frequencies of two types of modifiers used in the compliments are objects of analysis. Finally, the results of a previous study on American English compliment strategies is included in the comparison, to show whether the English compliment strategies used by EFL speakers and English native speakers differ.

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)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.877
Threshold uncertainty score0.926

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.018
GPT teacher head0.307
Teacher spread0.288 · 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 designNot applicable
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

Citations3
Published2019
Admission routes1
Has abstractyes

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