Beyond oneself: the ethics and psychology of awe
Bibliographic record
Abstract
Awe and wonder appear to be powerful emotions that can inform and shape our attitudes toward ourselves and others, especially in relation to the larger meaning and purpose of our lives. What are the psychological underpinnings of these universal emotions? How does awe, for example, relate to self-knowledge, and more generally to understanding the enigmatic contradictions of human nature? Is it possible to cultivate and develop this emotion as an ethical incentive in our relationships with others? Are awe and wonder capable of awakening and engendering moral transformation? Does the emotion of awe lie at the root of the religious impulse in humans? and Is there any room left for a sense of the miraculous in today's increasingly scientific and secular world? Professor of religious studies Lisa Sideris joins psychologists Jennifer Stellar and Piercarlo Valdesolo to explore how awe shapes our perspectives and views on everything from science to morality.
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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 | 0.007 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.064 |
| Scholarly communication | 0.008 | 0.011 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.006 |
| 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 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".