Although the GirlsTech model was designed specifically to target resources of high interest and great appeal to young women, it is important to understand that its use will identify resources likely to appeal to most, not all, young women, because not all young women exhibit gender-specific electronic information preferences. Gender-schema theory explains that sex is biologically determined and dichotomous, whereas gender is socially constructed and continuous. That is, genetic makeup determines whether a person is a woman or a man, but societal conditions result in a person’s viewing the world in gender-schematic or gender-aschematic terms. As Bem explained, “gender-schema theory proposes that sex-typing derives in large measure from gender-schematic processing, from a generalized readiness on the part of the child to encode and to organize information — including information about the self — according to the culture’s definitions of maleness and femaleness (1987, p. 231).” Gender-schematic, or sex-typed, individuals are those who view the world largely from a gendered point of view, bifurcating society into female and male components. Gender-aschematic, or non-sex-typed, individuals do not view the world in this generally bifurcated manner. Thus, a young woman who is strongly gender-schematic is likely to identify herself as a stereotypical young woman according to society’s general stereotype of the ideal female (i.e., nurturing, acquiescent, nonconfrontational, untalented in working with computers and technology, etc.) and consequently to have an attitudinal barrier against pursuing a computer-related career. “High-tech careers,” she would think, “are appropriate for young men, but not for me.” There is nothing biological that prevents the gender-schematic young woman from entering a high-tech career; the societally-nurtured attitude that she has adopted prevents her from doing so. Although the concepts of “sex” and “gender” should not be conflated, and it must be understood that electronic information preferences vary among young women, the goal of this research is to discover what aspects of electronic information resources are most likely to attract and to repel the greatest numbers of young women. As Cassell and Jenkins (1998) explained, to assert that all young women share the same preferences and wants is artificial, but necessary: “Despite the clear dangers of such ‘sweeping generalizations,’ the ability to determine what girls want may seem necessary at a time when we are trying to open up a space for girls to participate within this medium at all” (p. 25). Thus, the use of the GirlsTech model presented here is likely to identify resources of high interest to many, but not all, young women.