Giving vs. losing: age differences in decisions about charitable donations
Bibliographic record
Abstract
In addition to making decisions about gains and losses that affect only ourselves, often in life we make decisions that benefit others. Research on lifespan changes in motivation suggests that altruistic motives become stronger with age. However, few studies have explored the effect of age on decisions that affect others. The current study used a realistic financial decision making task involving choices for gains, losses, and donations. Each decision involved an intertemporal choice, in which the participant selected either a smaller-sooner or a larger-later option that could affect their bonus payout. Participants included 36 healthy younger adults (M = 25.1 years) and 36 healthy older adults (M = 70.4 years). Both age groups chose more larger-later donations than larger-later losses, but the magnitude of this effect was amplified in older relative to younger adults. These findings suggest that intertemporal choices may be sensitive to an age-related increase in altruistic motivation
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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 | 0.007 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".