Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it