The Effect of Prevention For Peer Bullying in Secondary School

DergiPark: 481899tmsjAims: Peer bullying is a frequent problem among adolescents. The aim of this study is to evaluate the effectiveness of complementary prevention for peer bullying in 11-14-year-old adolescents with family, teacher, student collaboration and to assess the effect of peer bullying on the quality of life. Methods: Seven hundred sixty students registered in school between ages 11-14, and who accepted to participate in the study were included in our investigation. Olweus Bully/Victim Questionnaire and Pediatric Quality of Life Inventory were used as data collection tools in the study. After the pre-test, school teachers and two selected students from each class; a total of 48 students were trained in peer bullying in small group. Interactive awareness activities were organized for the students at the school with all trained students and teachers to raise awareness of peer bullying. Afterwards, information brochures were distributed to the children and parents. 3 weeks after the training post-test was applied. The statistical evaluation of the study was carried out by using Chi-square and Student’s t-tests. Results: The questions about bullying and victimization were analyzed. In the study, the rate of victim students reduced from 43.2% to 30.4%; the rate of bully students reduced from 23.4% to 21.7%. There was a significant reduction in the rate of people involved in peer bullying. Nevertheless, Pediatric Quality of Life Inventory assessment of health-related quality of life in our group showed that the quality of life of students who were not involved in peer bullying was significantly higher. After our training, quality of life significantly increased in students who were not involved in bullying, compared to the ones who are involved in bullying. Conclusion: In our study group, it was observed that the quality of life of students who were not involved in peer bullying was significantly higher. The number of people involved in peer bullying decreased significantly. The low number of invalid surveys revealed that our research was successful in attracting the attention of the target group

Beyond the imitation game: Quantifying and extrapolating the capabilities of language models

Language models demonstrate both quantitative improvement and new qualitative capabilities with increasing scale. Despite their potentially transformative impact, these new capabilities are as yet poorly characterized. In order to inform future research, prepare for disruptive new model capabilities, and ameliorate socially harmful effects, it is vital that we understand the present and near-future capabilities and limitations of language models. To address this challenge, we introduce the Beyond the Imitation Game benchmark (BIG-bench). BIG-bench currently consists of 204 tasks, contributed by 442 authors across 132 institutions. Task topics are diverse, drawing problems from linguistics, childhood development, math, common-sense reasoning, biology, physics, social bias, software development, and beyond. BIG-bench focuses on tasks that are believed to be beyond the capabilities of current language models. We evaluate the behavior of OpenAI's GPT models, Google-internal dense transformer architectures, and Switch-style sparse transformers on BIG-bench, across model sizes spanning millions to hundreds of billions of parameters. In addition, a team of human expert raters performed all tasks in order to provide a strong baseline. Findings include: model performance and calibration both improve with scale, but are poor in absolute terms (and when compared with rater performance); performance is remarkably similar across model classes, though with benefits from sparsity; tasks that improve gradually and predictably commonly involve a large knowledge or memorization component, whereas tasks that exhibit "breakthrough" behavior at a critical scale often involve multiple steps or components, or brittle metrics; social bias typically increases with scale in settings with ambiguous context, but this can be improved with prompting

Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models

Language models demonstrate both quantitative improvement and new qualitative capabilities with increasing scale. Despite their potentially transformative impact, these new capabilities are as yet poorly characterized. In order to inform future research, prepare for disruptive new model capabilities, and ameliorate socially harmful effects, it is vital that we understand the present and near-future capabilities and limitations of language models. To address this challenge, we introduce the Beyond the Imitation Game benchmark (BIG-bench). BIG-bench currently consists of 204 tasks, contributed by 442 authors across 132 institutions. Task topics are diverse, drawing problems from linguistics, childhood development, math, common-sense reasoning, biology, physics, social bias, software development, and beyond. BIG-bench focuses on tasks that are believed to be beyond the capabilities of current language models. We evaluate the behavior of OpenAI's GPT models, Google-internal dense transformer architectures, and Switch-style sparse transformers on BIG-bench, across model sizes spanning millions to hundreds of billions of parameters. In addition, a team of human expert raters performed all tasks in order to provide a strong baseline. Findings include: model performance and calibration both improve with scale, but are poor in absolute terms (and when compared with rater performance); performance is remarkably similar across model classes, though with benefits from sparsity; tasks that improve gradually and predictably commonly involve a large knowledge or memorization component, whereas tasks that exhibit "breakthrough" behavior at a critical scale often involve multiple steps or components, or brittle metrics; social bias typically increases with scale in settings with ambiguous context, but this can be improved with prompting.Comment: 27 pages, 17 figures + references and appendices, repo: https://github.com/google/BIG-benc