Measuring Negative Emotion Differentiation Via Coded Descriptions of Emotional Experience
Why this work is in the frame
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Bibliographic record
Abstract
emotional experiences with a high degree of nuance and specificity. Research to date has almost exclusively focused on the former, with little attention paid to the latter. The current study sought to address this discrepant focus by testing two novel measures of negative ED (i.e., based on negatively valenced emotions only) via coded open-ended descriptions of individual emotional experiences, both past and present. As part of a larger study, 307 participants completed written descriptions of two negative emotional experiences, as well as a measure of emotion regulation difficulties and indices of psychopathological symptom severity. Negative ED ability, as measured via consistency between emotional experiences, was found to be unrelated to negative ED ability exhibited via coding of language within experiences. Within-experience negative ED may offer an incrementally adaptive function to that of ED between emotional experiences. Implications for ED theory are discussed.
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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 | 0.000 | 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) | 0.003 | 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 it