Awe and humility.
Bibliographic record
Abstract
Humility is a foundational virtue that counters selfish inclinations such as entitlement, arrogance, and narcissism (Tangney, 2000). We hypothesize that experiences of awe promote greater humility. Guided by an appraisal-tendency framework of emotion, we propose that when individuals encounter an entity that is vast and challenges their worldview, they feel awe, which leads to self-diminishment and subsequently humility. In support of these claims, awe-prone individuals were rated as more humble by friends (Study 1) and reported greater humility across a 2-week period (Study 2), controlling for other positive emotions. Inducing awe led participants to present a more balanced view of their strengths and weaknesses to others (Study 3) and acknowledge, to a greater degree, the contribution of outside forces in their own personal accomplishments (Study 4), compared with neutral and positive control conditions. Finally, an awe-inducing expansive view elicited greater reported humility than a neutral view (Study 5). We also elucidated the process by which awe leads to humility. Feelings of awe mediated the relationship between appraisals (perceptions of vastness and a challenge to one's world view) and humility (Study 4), and self-diminishment mediated the relationship between awe and humility (Study 5). Taken together, these results reveal that awe offers one path to greater humility. (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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".