Can repeated and reflective prosocial experiences in sport increase generosity in adolescent athletes?
Bibliographic record
Abstract
In partnership with a sport-based Experiential Philanthropy Intervention – The Play Better Program – we conducted a pre-registered, longitudinal experiment examining whether repeatedly reflecting on prosocial activity could boost adolescents’ objective generosity. Adolescents (N = 114; aged 9–16) practiced charitable giving throughout their 2-month sports season and were randomly assigned to repeatedly reflect on the importance of their prosocial activity (Reflection condition) or to write about their everyday activities (Control condition). Adolescents completed an objective measure of generosity at pre- and post-intervention and self-reported measures of prosocial character. Across conditions, adolescents donated objectively more at post- vs. pre-intervention. However, adolescents in the Reflection (vs. Control) condition were no more generous and did not report greater prosocial character at post-intervention. Overall, these findings highlight the malleability of human prosociality and the need for additional scholar-practitioner collaborations to uncover whether and how Experiential Philanthropy Interventions boost long-term generosity among the next generation of givers.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".