A Conversation Analysis on the Interview between Agnez Monica and Host in “Build Talk Show”
Bibliographic record
Abstract
The aim of the study is to determine how the aspects of conversational interactions are realized in the conversation. The researcher collects and analyzes data by applied qualitative content analysis through documentation technique. The data of this study were the utterances while the source of data is a video of the interview between the interviewer (Kevan Kenney) and the interviewee (Agnez Monica a.k.a Agnez Mo) in Build Talk Show. The source of the data was downloaded from the official Youtube channel of Build Talk Show with a duration of 27:03 minutes. The data analysis is based on the theory of conversation analysis proposed by Paltridge. The results of this study show that the interviewer (Kevan Kenney) employed the aspects of conversational interactions in asking and responding to the questions of the interviewee. The aspects of conversational interactions such as opening conversation, adjacency pairs, preference organization, turn taking, and feedback were used. Where as, closing conversation and repair categories were not used by interviewer throughout the conversation. On the other hand, the interviewee used Turn Taking, Feedback and Repair, but Opening and Closing Conversation, Adjacency Pairs, and Preference Organization were not used by the interviewee throughout the conversation. So, five of seven aspects of conversational interactions in conversation are applied. Those aspects of conversational interactions are realized in this conversation because it is the standard in conversation, and the interviewer and interviewee applied the aspects of conversational interactions in order to seek the information from the interviewee, to give the clarification of the issues and make a good communication in that conversation.
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 | 0.000 |
| 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 | 0.000 |
| 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 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".