Children’s giving: moral reasoning and moral emotions in the development of donation behaviors
Bibliographic record
Abstract
This study investigated the role of moral reasoning and moral emotions (i.e., sympathy and guilt) in the development of young children's donating behavior (N = 160 4- and 8-year-old children). Donating was measured through children's allocation of resources (i.e., stickers) to needy peers and was framed as a donation to "World Vision." Children's sympathy was measured with both self- and primary caregiver-reports and participants reported their anticipation of guilt feelings following actions that violated prosocial moral norms, specifically the failure to help or share. Participants also provided justifications for their anticipated emotions, which were coded as representing moral or non-moral reasoning processes. Children's moral reasoning emerged as a significant predictor of donating behavior. In addition, results demonstrated significant developmental and gender effects, with 8-year-olds donating significantly more than 4-year-olds and 4-year-old girls making higher value donations than boys of the same age. We discuss donation behaviors within the broader context of giving and highlight the moral developmental antecedents of giving behaviors in childhood.
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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".