Bibliographic record
Abstract
Complaining is a social act in which a speaker often verbally conveys feelings of suffering to gain empathy from listeners. The present study investigated the acoustic profile of complaints to identify which prosodic features are used in this context and to explore differences in their cultural expression in two variants of French. A stimulus set composed of 336 complaints and 336 prosodically neutral utterances produced by two cultural groups, French and Québécois (French-Canadian), was analyzed along 15 acoustic parameters. Utterances were also judged by listeners to determine whether complaints were perceptually associated with particular emotional characteristics. Relative to neutral statements, complaints displayed increases in fundamental frequency (mean, variability, and range), loudness, and high-frequency energy, and several rhythmic modulations. Complaints were also characterized by systematic changes in parameters related to voice quality and increased vocal control (decreased shimmer, increased harmonics-to-noise ratio), which could exemplify the speaker’s strategic use of emotive cues. Perceptually, complaining voices were most associated with sadness, anger, and surprise. Complaints produced by French and Québécois speakers demonstrated shared central tendencies but also differed both acoustically and perceptually. Our results provide new insights into the acoustic and perceptual profiles of emotive “complaining” speech patterns meant to elicit empathy in social interactions.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".