The dark side of the sublime: Distinguishing a threat-based variant of awe.
Bibliographic record
Abstract
Theoretical conceptualizations of awe suggest this emotion can be more positive or negative depending on specific appraisal processes. However, the emergent scientific study of awe rarely emphasizes its negative side, classifying it instead as a positive emotion. In the present research we tested whether there is a more negative variant of awe that arises in response to vast, complex stimuli that are threatening (e.g., tornadoes, terrorist attack, wrathful god). We discovered people do experience this type of awe with regularity (Studies 1 & 4) and that it differs from other variants of awe in terms of its underlying appraisals, subjective experience, physiological correlates, and consequences for well-being. Specifically, threat-based awe experiences were appraised as lower in self-control and certainty and higher in situational control than other awe experiences, and were characterized by greater feelings of fear (Studies 2a & 2b). Threat-based awe was associated with physiological indicators of increased sympathetic autonomic arousal, whereas positive awe was associated with indicators of increased parasympathetic arousal (Study 3). Positive awe experiences in daily life (Study 4) and in the lab (Study 5) led to greater momentary well-being (compared with no awe experience), whereas threat-based awe experiences did not. This effect was partially mediated by increased feelings of powerlessness during threat-based awe experiences. Together, these findings highlight a darker side of awe. (PsycINFO Database Record
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 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".