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Record W2122375127

A Contrastive Study in American and Japanese Addressing Strategies From the Perspective of Power and Solidarity

2014· article· en· W2122375127 on OpenAlexvenueno aff
Junying Kang

Bibliographic record

VenueStudies in literature and language · 2014
Typearticle
Languageen
FieldArts and Humanities
TopicSpanish Linguistics and Language Studies
Canadian institutionsnot available
Fundersnot available
KeywordsSolidarityPolitenessPerspective (graphical)Power (physics)SociologyLinguisticsFace (sociological concept)Contrastive analysisPerceptionSocial psychologyPsychologyPoliteness theoryPolitical scienceSocial scienceComputer sciencePolitics
DOInot available

Abstract

fetched live from OpenAlex

Addresses play an important role in human communication, the choice of which are universally determined by various contextual factors such as power, solidarity, distance, face and politeness, etc. but due to the different perceptions of these factors, addressing strategies vary from one culture to another. A contrastive study in American and Japanese addressing patterns within the framework of power and solidarity reveals that differences between the two addressing systems mainly occur in two aspects: the use of T and V address forms and the linguistic treatment for in-group and out-group members in terms of addresses. It is pointed out that the choice of address involves not only the consideration of the discrepancy among the politeness models and language practices, but also the knowledge of the cultural expectation and requirement of different cultures.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.014
Threshold uncertainty score0.028

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0030.003
Scholarly communication0.0020.002
Open science0.0010.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.000

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 head0.021
GPT teacher head0.316
Teacher spread0.295 · 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 source (direct Gemma or distilled Codex), 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

Citations3
Published2014
Admission routes1
Has abstractyes

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