Validation of Affective Sentences: Extending Beyond Basic Emotion Categories
Why this work is in the frame
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Bibliographic record
Abstract
We use nonverbal and verbal emotion cues to determine how others are feeling. Most studies in vocal emotion perception do not consider the influence of verbal content, using sentences with nonsense words or words that carry no emotional meaning. These online studies aimed to validate 95 sentences with verbal content intended to convey 10 emotions. Participants were asked to select the emotion that best described the emotional meaning of the sentence. Study 1 included 436 participants and Study 2 included 193. The Simpson diversity index was applied as a measure of dispersion of responses. Across the two studies, 38 sentences were labelled as representing 10 emotion categories with a low degree of diversity in participant responses. Expanding current databases beyond basic emotion categories is important for researchers exploring the interaction between tone of voice and verbal content, and/or people's capacity to make subtle distinctions between their own and others' emotions.
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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 | 0.006 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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