Children’s Emerging Understanding of Anonymity Does Not Predict Reputation Enhancing Generosity
Bibliographic record
Abstract
Young children tend to behave more generously when their actions are identified than when they are anonymous, yet we know little about the cognitive foundations required for anonymity to impact generosity. In three studies we examined Canadian children’s understanding of anonymity and its impact on sharing in anonymous and identified contexts. Study 1 assessed whether 3- and 5-year-old children (N = 100, 51 female) understood anonymous and identified sharing, and whether age-related changes in their understanding corresponded to sharing behavior. We found that understanding of anonymity improved with age, but anonymity did not influence sharing. Study 2 assessed 5-year-old children’s (N = 60, 30 female) judgments about how others would share in these contexts and their preferences for receiving donations from identified or anonymous donors. We found that children preferred to receive from identified donors and believed that identified donors were more generous. Study 3 assessed whether 5-year-old children (N = 60, 30 female) preferred to share as anonymous or identified donors themselves, and whether their choice influenced sharing behavior. We found that while participants preferred to share as identified donors, this choice did not influence sharing. Overall, our findings suggest that although 5-year-old Canadian children have a robust understanding of the implications of anonymous and identified sharing, this understanding is not sufficient to motivate increased generosity.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".