A cultural perspective on emotional experiences across the life span.
Bibliographic record
Abstract
Past research suggests that older adults place a greater priority on goals of maintaining positive experiences and distancing from negative experiences. We hypothesized that these aging-related differences in emotional experiences are more pronounced in Western cultures that encourage linear approaches to well-being compared with Eastern cultures that encourage more dialectic approaches to well-being. We compared reports of positive and negative emotional experiences from random samples of Americans (a culture characterized by focus on positive and distancing from negative experiences) and Japanese (a culture characterized by its endorsement of dialectical experiences). In support of our hypothesis, older Americans reported significantly less negative emotions in unpleasant situations, relative to their younger counterparts. Furthermore, both trait-level negativity (i.e., rumination) and interpersonal negativity (i.e., recall of unpleasant relationships and intensity of an unpleasant interpersonal experience) were lower among older compared with younger Americans. In contrast, such aging-related effects were absent in the Japanese respondents. Even though older and younger Japanese reported the same amount of negative emotions in unpleasant situations, older Japanese also reported more positive emotions in the same unpleasant situations. Together, these findings highlight the role of culture for understanding how emotional experiences unfold across adulthood.
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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 | 0.001 |
| 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 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".