Wanting to Get More or Protecting One’s Assets: Age-Differential Effects of Gain Versus Loss Perceptions on the Willingness to Engage in Collective Action
Bibliographic record
Abstract
OBJECTIVES: The present research examined motivational differences across adulthood that might contribute to age-related differences in the willingness to engage in collective action. Two experiments addressed the role of gain and loss orientation for age-related differences in the willingness to engage in collective action across adulthood. METHOD: In Experiment 1, N = 169 adults (20-85 years) were confronted with a hypothetical scenario that involved either an impending increase or decrease of health insurance costs for their respective age group. In Experiment 2, N = 231 adults (18-83 years) were asked to list an advantage or disadvantage they perceived in being a member of their age group. Subsequently, participants indicated their willingness to engage in collective action on behalf of their age group. RESULTS: Both experiments suggest that, with increasing age, people are more willing to engage in collective action when they are confronted with the prospect of loss or a disadvantage. DISCUSSION: The findings highlight the role of motivational processes for involvement in collective action across adulthood. With increasing age, (anticipated) loss or perceived disadvantages become more important for the willingness to participate in collective action.
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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 | 0.002 | 0.008 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".