Interaction Management in Nigerian Television Talk Shows
Bibliographic record
Abstract
Although there is a growing number of works on discourse analysis in Nigeria which covers classroom interactions, courtroom discourse, medical communication and media discourse, the language of television (TV) talk shows has not been fully explored. This study therefore, examined turn management in this genre. It identified the turn distribution strategies in Nigerian television talk shows and the contributions of these strategies to the management of the talks. Sacks, Schegloff and Jefferson’s Conversation Analysis served as our theoretical framework. Three Nigerian TV talk shows, namely, “Patito’s Gang”, “New Dawn with Funmi Iyanda” and “Inside Out” were selected for this study. Each selected talk show comprised four sampled episodes. “Patito’s Gang” from a private television station; “New Dawn with Funmi Iyanda” from a national television station and “Inside Out” from a private television station were purposively selected because they were handled by freelance presenters who were free from undue interference. Collection of data spanned four years: 2004-2008. And the analysis was both quantitative and qualitative. Generally, three turn distribution strategies were identified: Current-Speaker-Selects–Next-Speaker, Next-Speaker-Self-Selects-as-Next and Current-Speaker-Continues (where there is no pre-selection or self-selection). Current speaker selected next speaker by direct questioning, gaze and gestures. Next speaker self-selected as next through interruptions, overlaps, discourse markers, pauses and falling intonation. Where there was no pre-selection or self-selection at Transition Relevance Places, the current speaker continued after a pause of about half a second or more. These strategies enabled effective interaction management amongst the participants as turn allocation was not restricted but moderated by the hosts.
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 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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".