Psychopathology and Short‐term Emotion: The Balance of Affects
Bibliographic record
Abstract
In this study, the relationship between short-term emotion expressions and dimensional ratings of internalizing and externalizing symptomatology was examined. Short-term emotions, defined as facial or vocal displays of emotion generally lasting less than 10 seconds and elicited by a specific and proximal event, were observed during recess in 71 children from diverse socioeconomic backgrounds, who were between 4 and 8 years old. Internalizing and externalizing symptomatology was assessed through parent and teacher questionnaire. Sociometric ratings were obtained from peers on children's anger and aggression. It was hypothesized following Tomkins (1979) and others that one affect becomes predominant in the emotional experience of the individual. Different operationalizations of this concept were examined. Using regression analyses, externalizing symptomatology was found to be predicted by higher levels of anger, lower levels of happiness, and lower levels of sadness. Internalizing symptomatology was found to be predicted by higher levels of sadness and lower levels of anger. It was concluded on the basis of these data that the relationship between short-term emotion and internalizing and externalizing psychopathology is best understood as the balance between different short-term emotions. Results are discussed in the context of theories of emotion and their functions.
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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 | 0.000 | 0.002 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".