This paper explores the influence of materialism on consumer indebtedness among low income individuals who live in poor regions of Sao Paulo. A materialism scale was adapted to this context and used to describe the level of materialism among the population surveyed. Results obtained relative to the relationship between materialism and sociodemographic variables are compared to those of previous studies. A logistic regression model was developed in order to characterize individuals who have an installment plan payment booklet-the main source of consumer credit for the population studied-and to differentiate them from those who do not, based on the materialism level, sociodemographic variables and purchasing and consumer habits. The proposed model confirms materialism as a behavioral variable that is useful for forecasting the probability of an individual getting into debt in order to consume. Income had the biggest relative influence on the regression model, followed by materialism and age, controlled by gender.

OBJECTIVE:To describe opinions and attitudes concerning sexuality of the Brazilian urban population. METHODS:A population survey was carried out in 2005 on a representative sample of 5,040 interviewees. An analysis of the attitudes regarding sexual initiation and sexual education of teenagers, considering gender, age, schooling, income, marital status, color, geographic region and opinion on fi delity, homosexuality, and masturbation. The results were contrasted with a similar survey carried out in 1998, when possible. RESULTS:Most interviewees selected the "sex is evidence of love" option when describing the meaning of sex. As in 1998, the majority was in favor of sexual initiation after marriage (63.9% for women vs. 52.4% for men initiation); results differed among religions. School teenage education on the use of condoms was supported by 97% of the interviewees across all social groups. The proportion of Brazilians who agreed with having access to condoms in health services (95%) and at school (83.6%) was high. Fidelity remained an almost unanimous value and there was an increase, in 2005, in the proportion of those in favor of sexual initiation after marriage, and in the rate of acceptance of masturbation and homosexuality compared to the 1998 survey. The younger generations tend to be more tolerant and equalitarian. CONCLUSIONS:As observed in other countries, this study confi rms the diffi culty in establishing a single dimension that guides sexual life ("liberal" vs "conservative"). The study suggests that the normativity concerning sexual activity should be understood in the light of the local culture and social organization of sexuality, considered by the STD/Aids programs. Opinions in favor of free access to preservatives at school clash with the slower results obtained in fighting the stigma and discriminating against homosexual minorities. The design of laical policies on sexuality allow for the dialog across different perspectives.

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This paper investigates the relationship between electricity consumption, economic classification and household income, by means of comparing Brazilian Census Micro-Data with the customer database of AES Eletropaulo, a large Brazilian electric distribution company, using traditional statistics and spatial auto-regressive models. Income and economic classification are recognized as efficient proxies for purchasing power. Income indicators based on Electricity Consumption can be almost automatically generated by electric companies using GIS techniques, and this is a potential new business model for electric companies.

Semantic annotation, the process of identifying key-phrases in texts and linking them to concepts in a knowledge base, is an important basis for semantic information retrieval and the Semantic Web uptake. Despite the emergence of semantic annotation systems, very few comparative studies have been published on their performance. In this paper, we provide an evaluation of the performance of existing systems over three tasks: full semantic annotation, named entity recognition, and keyword detection. More specifically, the spotting capability (recognition of relevant surface forms in text) is evaluated for all three tasks, whereas the disambiguation (correctly associating an entity from Wikipedia or DBpedia to the spotted surface forms) is evaluated only for the first two tasks. Our evaluation is twofold: First, we compute standard precision and recall on the output of semantic annotators on diverse datasets, each best suited for one of the identified tasks. Second, we build a statistical model using logistic regression to identify significant performance differences. Our results show that systems that provide full annotation perform better than named entities annotators and keyword extractors, for all three tasks. However, there is still much room for improvement for the identification of the most relevant entities described in a text.