The Persistence Of Traditional Gender Stereotypes: Evidence From The Distribution Of Academic Honors At A Female-Majority University

A shift from male-majority to female-majority university campuses has opened up new areas for research on gender bias, stereotypes, and discrimination. At one large state university on the west coast, there were more female than male graduates in Spring, 2008 in 7 out of 8 colleges, including the traditionally male-majority areas of business and science.  Relative probabilities for men and women of receiving honors in each major field of study at this school, compared to national data of gender breakdowns by field in 1980, showed that men and women were still relatively more likely to receive honors in fields that were traditionally male and female, respectively. Findings also cast doubt upon Kanter’s tokenism hypothesis. Curiously, it was traditionally female, not male, fields that had the highest levels of gender inequity, though gender inequity overall may be on a decline. More research is needed to identify why this difference between gender and honors still exists. Universities should also be aware of the continuing potential for subtle gender discrimination, even in fields where equal numbers of men and women participate

Partial Information Decomposition as a Unified Approach to the Specification of Neural Goal Functions

In many neural systems anatomical motifs are present repeatedly, but despite their structural similarity they can serve very different tasks. A prime example for such a motif is the canonical microcircuit of six-layered neo-cortex, which is repeated across cortical areas, and is involved in a number of different tasks (e.g.sensory, cognitive, or motor tasks). This observation has spawned interest in finding a common underlying principle, a 'goal function', of information processing implemented in this structure. By definition such a goal function, if universal, cannot be cast in processing-domain specific language (e.g. 'edge filtering', 'working memory'). Thus, to formulate such a principle, we have to use a domain-independent framework. Information theory offers such a framework. However, while the classical framework of information theory focuses on the relation between one input and one output (Shannon's mutual information), we argue that neural information processing crucially depends on the combination of \textit{multiple} inputs to create the output of a processor. To account for this, we use a very recent extension of Shannon Information theory, called partial information decomposition (PID). PID allows to quantify the information that several inputs provide individually (unique information), redundantly (shared information) or only jointly (synergistic information) about the output. First, we review the framework of PID. Then we apply it to reevaluate and analyze several earlier proposals of information theoretic neural goal functions (predictive coding, infomax, coherent infomax, efficient coding). We find that PID allows to compare these goal functions in a common framework, and also provides a versatile approach to design new goal functions from first principles. Building on this, we design and analyze a novel goal function, called 'coding with synergy'. [...]Comment: 21 pages, 4 figures, appendi

What’s Good in Theory May Be Flawed in Practice: Potential Legal Consequences of Poor Implementation of a Theoretical Sample

This article discusses the problems with the use of statistical sampling in litigation. Sample-based research is increasingly used in a diverse array of cases including products liability, antitrust, intellectual property, and criminal law. Sample-based research provides objective evidence upon which decisions, damages, and liability may rest. Despite its importance, however, statistical evidence is often misused and misunderstood by attorneys unfamiliar with the underlying form of analysis. This article explores common errors when using litigative samples, comments upon best practices for the use in law of sample-based research, and demonstrates the importance of sound statistical sampling and data collection in a recent case