Tactics of Verbal Defense Strategy in Youth Discourse and Their Representation in Modern Talk Shows
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".