Influence of Experience of Positive Commenting Activity (Sunfull) on Aggression and Hate Speech of Korean Adolescents
Bibliographic record
Abstract
This study began in recognition of societal problems such as aggression and hate speech due to the spread of cyber-violence and malicious commenting. Hence, this study was conducted to verify whether positive commenting activity acts as a means of controlling adolescents’ aggression and hate speech. With this, a survey was conducted on 410 middle and high school students registered in the Sunfull Foundation. Consent from the organization's head, participant and research purpose anonymity, as well as data confidentiality, were all stated in advance taking the study's ethical considerations into mind. Frequency analysis and regression analysis were performed on the collected data using SPSS 22.0 PC ver. As a result, this study found that adolescents’ overall aggression decreased, and in particular, the more they participated in the positive commenting activity and the longer the period of participation in positive commenting, the lower the overt aggression of adolescents. These findings suggest that organizing positive commenting activities as one of the required subjects in the school curriculum may be a more useful strategy given the scenario that students in today's classrooms are in, which is one of continual academic pressure. Therefore, various support should be provided as well as social interest in the use of positive language as well as in the level of ethics education for adolescents.
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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 | 0.001 | 0.004 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".