Turn-Taking in Political Interviews and Its Impact on Building Government Credibility
Bibliographic record
Abstract
Television political inquiry is conducted through question-and-answer exchanges with host and government officials on prominent livelihood issues, which helps to improve the efficiency of government governance. Interview participants’ identity, communicative intention, power, and contextual factors have a significant impact on the discourse choices of both parties. This research clarifies the mechanism by which communicators mobilize appropriate discourse strategies to promote the solution of social problems. It adopts qualitative research method and conversation analysis to interpret interview participants’ talk-in-interaction and discourse strategies in “Ask Government Affairs of Shandong”. Research finds that adjacency pairs in political interview perform social behaviors such as greetings, requests, suggestions, apologies, acknowledgement. Interview participants’ turn-taking is divided into claiming for the turn, holding the turn, and giving up the turn. Discourse strategies such as insertion, interruption, and repetition are adopted to claim for the turn. Discourse markers and conversation repair are applied in communicators’ turn-holding stage. Communicators adopt explicit nominations, vague job designations, and silence strategies to give up the turn. Clear, accurate, and highly relevant official responses are conducive to projecting a responsible government image and improving government credibility. The phenomenon of avoidance, hesitation, and pause in official response is likely to cause the masses to question government officials’ work capability, which is not conducive to establishing a positive and trustworthy government image. Suggestions for optimizing the process setting of political interview and official responses are provided to enhance the effect of political enquiry.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".