“I wanted to hurt her”: Children’s and adolescents’ experiences of desiring and seeking revenge in their own peer conflicts
Bibliographic record
Abstract
Abstract This study examined children's and adolescents’ descriptions of wanting and seeking revenge in peer conflicts. A total of 100 youth divided into three age groups (7‐, 11‐, and 16‐year‐olds) were interviewed about experiences in which they wanted to get back at a peer who harmed them. Most youth recalled experiencing retaliatory desires, but typically indicated that such desires were not acted out; 7‐year olds were less likely than older youth to describe carrying out their retaliatory desires. Youths’ reasons for seeking revenge versus containing their retaliatory desires revealed age effects in their thinking about their own retaliation. Younger children's reasoning focused on the undesirability of harming others and potential punishments that could ensue, but they generally did not coordinate these concerns with the fact that they themselves had just been harmed. In contrast, older youth described their own retaliatory actions as driven by goals stemming from being deeply hurt, but such goals were balanced against their self‐protective motives, sensitivity to the particularities of transgressions, and self‐reflective moral commitments. Findings underscore that desires for revenge can be considered to be a part of children's experiences of conflict, but also crucially, that youth recognize their own capacities to contain and redirect these desires.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".