Receiving cybergossip: Adolescents’ attitudes and feelings towards responses
Bibliographic record
Abstract
The purpose of this study was to understand youth's attitudes and emotions towards responses to cybergossip. Youth (N = 160, ages 10–16) read ten stories involving cybergossip, where an individual received gossip electronically from a friend. The target of gossip was either another friend or a classmate (Target Relationship: friend/classmate). The gossip receiver responded to the gossip sharer in five different ways (Response: passive/positive intervention/negative intervention/encouraging/blocking). In addition, as a between-subjects factor, the online setting was either a private conversation between the sharer and receiver or a public setting involving the sharer, receiver, and a few other friends in a group chat (Setting: private/public). Age (preadolescent/adolescent) and gender (female/male) differences were also examined. Participants were asked to morally evaluate each response, rate the effectiveness of each response, and rate their emotions towards using each response. The findings highlight the nuances of responding to cybergossip and the role of personal and contextual factors. Moreover, the results suggest there are misconceptions among youth about the effectiveness of interventions. These results and others as well as the implications will be discussed. The findings provide critical information on what youth in today's digital world believe are acceptable and effective ways to cyber-communicate.
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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 | 0.007 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".