To be fair, generous, or selfish: The effect of relationship on Chinese children’s distributive allocation and procedural application
Bibliographic record
Abstract
Abstract Previous research has found that children’s sharing with others relies on fairness norms, but also varies according to their social relationships. The current study focuses on the conflict between fairness and relationship, exploring their impacts across two resource allocation contexts. We used a parallel work task to explore the effect of relationship with different recipients (friend, stranger, or disliked peer) on three allocation patterns (generous, fair, or selfish), when children directly allocated resources (distributive allocation), or applied different procedures to recipients (procedural application). Participants consisted of 123 Chinese children between the ages of 6 and 12. We found that in the distributive allocation context, in which participants directly decided the outcome, children primarily considered their relationship with recipients when dividing resources, not fairness. However, in the procedural application context, in which children could choose different allocation procedures for recipients, children primarily preferred fairness, regardless of social relationship. Moreover, when making distributive allocations, 6‐ to 8‐year‐olds were more selfish toward their disliked peers, whereas 9‐ to 12‐year‐olds tended to be more fair and generous toward their friends and strangers. These findings shed light on the link between social relationship and fairness within different allocation contexts among children of Chinese cultural background.
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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 | 0.003 | 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 | 0.001 |
| 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".