What would you do? Children’s hypothetical responses to hearing negative and positive gossip involving friends and classmates
Bibliographic record
Abstract
The current study examined children and adolescents’ hypothetical responses to hearing negative or positive gossip shared by a friend or a classmate that targeted either a friend or a classmate. Participants ( N = 134, ages 8–16) read eight stories and were asked to take the perspective of the gossip listener and indicate how they would respond, a 2 (valence: negative or positive) × 4 (relationship type: friend or classmate of the sharer and target) design. Participants’ responses to how they would react were coded as encouraging, neutral, or discouraging. The findings showed that negative gossip shared by a classmate that targeted a friend had more discouraging responses than negative gossip shared by a friend targeting a classmate. Furthermore, positive gossip shared by a friend that targeted another friend had more neutral responses than positive gossip shared by a classmate that targeted a friend or another classmate, which had more encouraging responses. Age and gender differences revealed that adolescents provided more neutral responses overall, compared to children, and girls provided more discouraging responses for negative gossip than boys. This study provides information on how children and adolescents think they should respond to gossip. This can help school professionals address neutral attitudes toward gossip and prevent engagement in gossip behavior.
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 | 0.007 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".