Bibliographic record
Abstract
Abstract Research on the apology spans over half a century and has been quite prolific. Yet, a major issue with numerous studies on apologies is a lack of findings from naturally occurring interaction. Instead many studies examine written elicitations. As a result they research how respondents think they apologize, not how they do apologize. This project, in contrast, stresses the importance of studying the apology as a dynamically constructed politeness strategy in situated interaction. Apologies are part of the ever-present relational work, i.e., co-constructed and co-negotiated, emergent relationships in a situated social context. Hence, the focus is not on the illocutionary force indicating device (IFID) alone, nor on the turn in which the IFID is produced, but on the interactional exchange in situ. Naturally, data eliciting produces a larger sample size of apologies than the taping and transcribing of naturally occurring interaction does. To remedy the issue, this study uses interactions from situation comedies, which provide a large sample of apologies in their interactional context. Sitcom interactions constitute a valid focus of pragmatic research as they share fundamental elements of natural interactions ( B. Mills 2009 ; Quaglio 2009 ). The validity of this approach is tested using findings from published conversation analytic studies on apologies. The analysis is set within the framework of discursive pragmatics and leads to new insights on apologies and responses to apologies.
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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 | 0.004 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.014 | 0.004 |
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".