Metaphorical and literal profiling in the study of emotions
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
This paper focuses on the conceptualization of anger as viewed from two disciplinary perspectives: Conceptual Metaphor Theory and emotion psychology. In the first study, twenty varieties of anger lexicalized in three languages (English, Russian, and Spanish) are characterized using the Metaphorical Profile Approach, a quantitative corpus-based assessment of the meaning of emotion words in metaphorical contexts. In the second study, the same set of lexemes is analyzed using a psycholinguistic feature-rating instrument adapted to the study of near-synonyms. Our results demonstrate congruence of the two methods in unveiling the internal organization of the anger family of terms in each language, and the reasons for this organization. In particular, the metaphorical and the feature-based profiles provide consistent insight about variation in bodily heat, expressiveness, regulation, action tendencies (aggression and drive to act), regulation, and the temporal characteristics of anger experiences. To conclude, we discuss the mutual complementarity of the two profiling methodologies and their relevance for a wider research context.
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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 | 0.001 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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