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Record W199897549

A Study of the Verbal Conflict between Mother and Daughter-in-law in Desperate Housewives ETUDE DU CONFLIT VERBAL ENTRE LA MÈRE ET LA BELLE-FILLE DANS LES MAÎTRESSES DE MAISON DÉSESPÉRÉES

2008· article· fr· W199897549 on OpenAlexvenueno aff
Ding Wang, Jin’an Hou

Bibliographic record

VenueCross-cultural communication · 2008
Typearticle
Languagefr
FieldArts and Humanities
TopicLanguage, Discourse, Communication Strategies
Canadian institutionsnot available
Fundersnot available
KeywordsDaughterHarmony (color)ConversationStyle (visual arts)NegotiationPsychologyFace (sociological concept)Cultural conflictSociologyTelevision seriesSocial psychologyLawPolitical scienceMedia studiesArtLiteratureAnthropologyCommunicationSocial science
DOInot available

Abstract

fetched live from OpenAlex

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.

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 categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesScience and technology studies
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.255
Threshold uncertainty score1.000

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 studies0.0020.005
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
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 head0.053
GPT teacher head0.344
Teacher spread0.291 · 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; both teacher heads agree on what is shown here.

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
Published2008
Admission routes1
Has abstractyes

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