The Role of Gender in TV Talk Show Discourse in Bangladesh: A Conversational Analysis of Hosts’ Interaction Management
Bibliographic record
Abstract
Over the years of research on gender and language, a growing interest has developed in the study of gender differences and differences in verbal interactions. However, TV talk-shows are a relatively less studied area of pragma-linguistics. TV talk shows are like everyday face-to-face talks except that they take place in an institutional setting. They include all the major features of conversations wherein turn-taking is a salient component of conversational interactions. Based on Holmes’ six universals about language and gender that stood against Lakoff’s Deficit Model, this study examined four episodes from four TV talk-shows in Bangladesh, two being hosted by men and two by women, to determine how differentially the hosts take turns to manage their verbal interactions in their talk shows. This study employs the conversation analysis approach developed by Sacks, Schegloff, and Jefferson to examine how the hosts’ turn-taking overlaps with guests’ speeches, and how the hosts’ practices of interruptions, based on gender, are shaped with distinct functions to manage their interactions in talk shows. Data analysis shows that the female hosts, aligned with Holmes’ universals, managed interactions by soft transitions, minimal turns with supportive overlaps, the strategy of co-construction, and nonlinguistic back channels whereas the male hosts’ interaction management patterns were fully opposite from each other’s: one took excessive turns mostly characterized by interruptive overlaps while the other, like the female hosts, made soft transitions and avoided interruptive turns. This study adds to gender and language studies contributing to emerging social perceptions that woman verbal interactions are characterized by solidarity and co-operation despite their social high standing.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.003 | 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".