Moderators of curiosity and information seeking in younger and older adults.
Bibliographic record
Abstract
= 514) rated their curiosity about content before having the opportunity to seek out more information. Experiment 1 examined the impact of social value on curiosity and information seeking about trivia. Online popularity metrics served as social value cues. Metric visibility increased engagement with high-popularity information for older adults, whereas it decreased engagement with low-popularity information for younger adults. Experiment 2 examined the impact of practical value on curiosity and information seeking about science facts. Personal and collective practical value were highlighted by linking the information to the domains of medicine and the environment, respectively. Patterns of curiosity and information seeking revealed greater sensitivity to collective practical value in older than younger adults. In both experiments, the relationship between curiosity and information seeking was stronger in older adults than in younger adults. Overall, these findings suggest that age differences in motivational priorities may lead to age differences in curiosity and information seeking. In addition to highlighting strategies for fostering curiosity in older learners, these findings may also inform digital literacy interventions aimed at reducing engagement with clickbait and misinformation. (PsycInfo Database Record (c) 2024 APA, all rights reserved).
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".