The role of positive emotions and ego-resilience in personal strivings.
Bibliographic record
Abstract
Using the broaden-and-build theory of positive emotions (Fredrickson, 1998) as a foundation, this research examined the role of individual differences in positive emotions and ego-resilience in personal striving and eudaimonic well-being in two separate, but related studies. In Study 1, 182 undergraduate students (48 men 134 women) completed measures of positive emotions, ego-control, ego-resilience, personal strivings, and eudaimonic well-being. Ego-resilience was related to the proportion of self-transcendent goals, and both positive emotions and ego-resilience were related to goal ratings of positivity, personal growth, and personal expressiveness, goal progress, and eudaimonic well-being however, regression analyses revealed that ego-resilience mediated the relationship between positive emotions and these goal striving variables. In Study 2, 133 (37 men 96 women) undergraduate students completed measures of ego-resilience and positive emotions, after which they were randomly assigned to view a short positive emotion inducing video (designed to elicit either amusement or awe), or a neutral (control) video and then asked complete a measure in which they were asked to outline several personal goals they would like to accomplish within four weeks. Four weeks later, participants returned to the lab to complete a follow-up assessment of their goal progress, measures of eudaimonic well-being, positive emotions, and perceived stress, as well as a re-assessment of their level of ego-resilience. The results of the emotion manipulation revealed that individuals in the awe condition reported significantly more personal growth goals. Self-reported positive emotions both at Time 1 and over the intervening four weeks predicted increase in ego-resilience supporting Fredrickson's (1998) hypothesis that positive emotions build' resources and character strengths. Ego-resilience did not interact with emotion condition to predict goal striving however, ego-resilience again partially mediated the relations
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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 | 0.004 |
| 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".