To err is human: Forgiveness across childhood and adolescence
Bibliographic record
Abstract
Abstract This study examined children's and adolescents' narrative accounts and evaluations of an instance when they forgave a peer and an instance when they did not forgive, as well as their definitions of what it means to forgive. The sample included 100 participants in three age groups (7‐, 11‐, and 16‐year olds). Regardless of age, forgiveness and non‐forgiveness accounts differed in interpersonal features, such as how they responded when hurt and whether the peer apologized. The psychological features of the experiences involving their own thoughts and feelings also distinguished between events that were forgiven and those that were not, but did so for 16‐year olds and, sometimes, for 11‐year olds, but never for 7‐year olds. The distinct ways in which younger and older children narrated their experiences also were reflected in their evolving definitions of what it means to forgive, though children's definitions revealed aspects of their thinking not captured in their narratives. Finally, children at all ages judged forgiving favorably but, with age, their evaluations of not forgiving became less negative. These findings challenge the narrow conceptual and methodological lenses through which forgiveness had been examined, and underscore meaningful age differences in the ways children make sense of and evaluate forgiveness and non‐forgiveness.
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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 | 0.007 |
| 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 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 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".