Factors in the perception of speaker politeness<b>: the effect of linguistic structure, imposition and prosody</b>
Bibliographic record
Abstract
Abstract Although linguistic politeness has been studied and theorized about extensively, the role of prosody in the perception of (im)polite attitudes has been somewhat neglected. In the present study, we used experimental methods to investigate the interaction of linguistic form, imposition, and prosody in the perception of (im)polite requests. A written task established a baseline for the level of politeness associated with certain linguistic structures. Then stimuli were recorded in polite and rude prosodic conditions and in a perceptual experiment they were judged for politeness. Results revealed that, although both linguistic structure and prosody had a significant effect on politeness ratings, the effect of prosody was much more robust. In fact, rude prosody led in some cases to the neutralization of (extra)linguistic distinctions. The important contribution of prosody to (im)politeness inferences was also revealed by a comparison of the written and auditory tasks. These findings have important implications for models of (im)politeness and more generally for theories of affective speech. Implications for the generation of Particularized Conversational Implicatures (PCIs) of (im)politeness are also discussed.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".