‘D'you understand that honey?’: Gender and participation in conversation
Bibliographic record
Abstract
Introduction This chapter focuses on a single turn-at-talk produced during the course of a backyard barbecue. I argue that this turn, ‘D'you understand that honey?’, can be seen not only to invoke the relevance of the recipient's gender but also, simultaneously, to formulate the kind of talk it refers to by ‘that’ – a dirty joke – as designed for an exclusively male audience. Though the talk in question contains no explicit mention of ‘men’ or ‘woman’ or ‘girls’, etc. – that is, though it contains no explicitly gendered referring expressions – it nevertheless serves to highlight this aspect of the context. In this chapter, then, the gender of the participants is conceptualized as a feature of the context which is always available but not always relevant. Rather, I suggest that it takes work to push gender from the taken-for-granted, seen but unnoticed backdrop into the interactionally relevant foreground of oriented-to features of the setting (Hopper & LeBaron, 1998). One way this happens is by talk, such as ‘Do y'understand that, honey?’, which links the organization of participation in the activity of the moment – here reception and appreciation of a dirty joke – to larger, socially significant categories such as those of ‘men’ and ‘women’. Prompted in part by Schegloff's (1997; 1998b) reply to Wetherell, as well as by Schegloff's earlier programmatic papers on ‘social structure’ (1991), a number of recent studies have advocated a specifically conversation analytic (CA) approach to gender which attends to participants' displayed orientation to gender-relevant categories as these are revealed in their own conduct (see inter alia Kitzinger, 2000a; 2005b; Sidnell, 2003; Speer, 2002a; 2005a; 2005b; Stokoe, 1998; Stokoe & Smithson, 2001; West & Zimmerman, 1987).
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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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.019 |
| Scholarly communication | 0.012 | 0.009 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".