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Record W2148048683 · doi:10.5539/ass.v11n6p132

Tactics of Verbal Defense Strategy in Youth Discourse and Their Representation in Modern Talk Shows

2015· article· en· W2148048683 on OpenAlexvenueno aff
Bekhanova Zhazira Erbulatovna, Ryssaldy Kussain Tinisbaevich

Bibliographic record

VenueAsian Social Science · 2015
Typearticle
Languageen
FieldComputer Science
TopicDigital Communication and Language
Canadian institutionsnot available
Fundersnot available
KeywordsFalse accusationSolidaritySocial psychologyPsychologyEthnic groupRepresentation (politics)LinguisticsPolitical sciencePoliticsLaw

Abstract

fetched live from OpenAlex

The article analyzes youth discourse and its social features in popular TV genre talk show. Each participant of the show has his/her own function and definite part prepared by the authors. According to the talk show analysis, it was defined that participants of this age group are mostly presented as victims of any case, as accused people who are discussed by public, criticized by experts and audience. Thus, in most cases the youth follow the communication strategy of verbal defense, and particularly interdefense of the members of their own peer-group. The strategy shows inner solidarity and intimacy which connect the youth and are obvious in their speech. As a result, we have defined precise four tactics of the communication strategy of verbal defense: a tactic of praising a discredited person, a tactic of opposing the accusation, a tactic of transmitting of accusation to other parties, and a tactic of counter-accusation. All the situations are presented as very close to real-life situations of youth discourse in intra-dimension level when youngsters interact with the representatives of other age groups. There have been given several extract from popular talk shows in Kazakh, Russian and English which prove the hypothesis that youth discourse is characterized by the same communication strategies and tactics. These peculiarities do not depend on nationalities or ethnic groups the participants belong to.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.631
Threshold uncertainty score0.196

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 communication0.0000.001
Open science0.0010.000
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 head0.059
GPT teacher head0.331
Teacher spread0.272 · 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

Citations1
Published2015
Admission routes1
Has abstractyes

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