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Record W7096652094

2008,Vol.4,No.1,1-7 Canadian Academy of Oriental and Occidental Culture 1 A Study of the Verbal Conflict between Mother and Daughter-in-law in Desperate Housewives

2007· article· en· W7096652094 on OpenAlexaboutno aff

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldArts and Humanities
TopicLanguage, Discourse, Communication Strategies
Canadian institutionsnot available
Fundersnot available
KeywordsConversationHarmony (color)Style (visual arts)Cultural conflictFace (sociological concept)Social conflict
DOInot available

Abstract

fetched live from OpenAlex

Abstract: Ting-Toomey proposed face-negotiation theory in the 1980s. For years, face-negotiation theory has been used to explain conflict in communication and cultural differences related to communication. As we know, conflict is a very natural phenomenon in the conversation of different cultures. However, people have different ways to solve the conflict. This paper studies the conflict between mother and daughter-in-law in TV series. The extracts come from the American TV series Desperate Housewives. The paper aims to answer the two research questions: (1) does the conflict style between family members like mother and daughter-in-law in American TV series Desperate Housewives is also dominating? (2) If it is, how the conversation proceeds under the dominating style? After analyzing the materials, we find that the conflict style between mother and daughter-in-law in American TV series Desperate Housewives is dominating. In addition, although the conflict style between mother and daughter-in-law in American TV series Desperate Housewives is dominating, people also emphasize on the social harmony among family members. Key words: conflict, face-negation theory, mother and daughter-in-law Résumé: Ting-Toomey propose la théorie de la négation de face dans les années 1980. Depuis des années, la théorie de la négociation de face a été utilisée pour expliquer les conflits dans la

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.641
Threshold uncertainty score0.865

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.042
GPT teacher head0.298
Teacher spread0.256 · 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

Citations0
Published2007
Admission routes1
Has abstractyes

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