Replication Study to Evaluate the Effects of Awe on Humility
Bibliographic record
Abstract
This paper replicates Stellar and Colleagues’ 2018 study involving an experimental manipulation of awe using standardized video induction, as well as proposing and testing out an additional hypothesis. The previous study hypothesized that watching an awe-inducing video would lead participants to write fewer strengths before writing their weaknesses. In addition to the replication, we hypothesized that participants with higher scores of depressive symptomatology (DS) would list fewer strengths due to diminished self-concept and self-efficacy. Ninety-four undergraduate psychology students were recruited from McGill University ranging from 18 to 35 years of age. Participants were randomly assigned to either the awe-inducing or neutral video condition, and then filled out measures of humility, emotional reactions, and DS. In contradiction with the original study, participants in the awe condition and the neutral condition did not significantly differ in their ratio of disclosed strengths to weaknesses, therefore no significant correlations were found between awe and humility or humility and depression. Additionally, results indicated that participants with greater DS did not list fewer strengths compared to those scoring lower on the CES-D. We were unable to directly replicate the original study and thus rejected our alternate hypothesis. This study had various potential limitations, among which are the possibilities of self-report bias, issues regarding convenience sampling, and bias due to time constraints. The current study advances the literature by including depression relating to awe and humility. Further research is needed, to differentiate lab-induced awe from natural experiences of awe and identify possible moderating factors on humility.
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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 | 0.025 | 0.061 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".