Reported Causal Antecedents of Discrete Emotions in Late Life
Bibliographic record
Abstract
Valuable insights about emotional well-being can be learned from studying older adults who have wrestled with differentiating and regulating their emotions while they navigate through the many joys and traumas of a lifetime. Our objective was to document the underlying reasons for older adults' (n = 353, ages 72 -99) emotional experiences. Using a phenomenological approach, we identified participants' reported reasons (i.e., antecedents) for a broad variety of positive and negative emotions, classifying them into thematic categories through a content analysis. The array of thematic categories that emerged for some emotions was more differentiated than for others. For example, 14 antecedent categories were required to account for the emotion of happiness; whereas, only 4 categories were needed to capture all antecedents for anger. Our analysis provided a rich description of what older adults report as the causes of their emotions, showing that later life is characterized as a time when the loss of love ones elicits sadness, self-limitations elicit frustration, and others' transgressions elicit anger. Yet, our data show that old age can be portrayed even more so as a time when a variety of positive emotions are elicited by social factors (interactions and relationships), achievements, and personal attributes. Finally, in an analysis of the most common antecedents for pride (accomplishments) and anger (other's transgression), we suggest that pride over accomplishments is most likely elicited by internal attributions to skill and effort; whereas, anger over others' transgressions is most likely elicited by controllable attributions to the transgressor's inconsiderate or offensive behavior. Overall, this shows the utility of applying Weiner's attributional framework (Weiner, 1985) to an analysis of emotion antecedents in late life.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".