Addressing Relational Aggression: Assessing the Merits of Coeducational and Gender-Specific Bullying Prevention Programs

Interviews with eight bullying prevention program directors from around the country reveal the extent to which research findings are reflected in bullying prevention programs currently in operation. Framed as a list of best practices for coeducational and gender-specific bullying prevention curricula, the purpose of the present thesis was to document the most positive contributions made by various approaches to bullying prevention programs to the overall field, and to highlight practices of programs that reflect insight into what is known about gender differences in bullying. Best practices included new approaches to empathy-building, service-learning, confidentiality, cyberbullying, positive reinforcement, reporting systems and youth-driven programming. This thesis will demonstrate that only some bullying prevention programs take advantage of scholarly knowledge by incorporating recent findings into their curricula. The results demonstrate that while many programs incorporate known literature about bullying into their programs--resulting in positive contributions to the field--others may be applying potentially harmful practices. Research findings also revealed that new best practices were present in programs regardless of status as a gender-specific or coeducational audience

Learning Representations that Support Extrapolation

Extrapolation -- the ability to make inferences that go beyond the scope of one's experiences -- is a hallmark of human intelligence. By contrast, the generalization exhibited by contemporary neural network algorithms is largely limited to interpolation between data points in their training corpora. In this paper, we consider the challenge of learning representations that support extrapolation. We introduce a novel visual analogy benchmark that allows the graded evaluation of extrapolation as a function of distance from the convex domain defined by the training data. We also introduce a simple technique, temporal context normalization, that encourages representations that emphasize the relations between objects. We find that this technique enables a significant improvement in the ability to extrapolate, considerably outperforming a number of competitive techniques.Comment: ICML 202

NFS with only the subjects who went through the generalization test

A linear mixed model for the number of failed sequences of the tests with only the subjects who went through the generalization test</p