Face Value in Conversational Closings: Insights from Desperate Housewives
Bibliographic record
Abstract
This study investigates face value in conversational closings in American soap opera Desperate Housewives. Using qualitative method combined with quantitative method, we collected altogether 52 scripted conversation excerpts among the five leading characters and analyzed them within the thoeretical framework of face and politeness theory (Brown & Levinson, 1987). Analyses of the scripted conversations showed that conversational closings in Desperate Housewives consist of three main patterns, closing, pre-closing (+insertion) + closing (+after-close), and leave-taking. Most pre-closing and closing strategies involve face consideration, with the most frequently used ones being “giving reasons”, “mentioning a future relationship”, “discourse markers”, and “apology for leaving”. These strategies are deployed to save either the positive face or the negative face of the other party. Some conversations end with one party’s walking away due to anger or pique, consequently threatening the other’s face, but it occurs in irregular situations for dramatic effects. The findings suggest that closing a conversation can threaten both the positive face and negative face of the other party, and that in doing so, even familiar people or friends are concerned about interlocutors’ “face” or “face-saving” by deploying some strategies.This study contributes to both the conversational closings study and face and politeness study.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".