994 resultados para Popular class


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Twin Peaks arguably paved the way for the television programmes currently popular with adolescent audiences, like The OC and Veronica Mars and, in it, many of the issues and representational strategies in those later programmes have their earlier manifestation. Specifically, the Twin Peaks plotline evinces a set of cultural anxieties about class-difference. Twin Peaks creates a cultural microcosm of American society that is paradoxically writ large by the limited parameters of an isolated community. Within a constricted space, characters are depicted as both individuals and as archetypes of a class location.

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In this article, I analyze a class of contest success functions (CSFs) that satisfy Luce's Choice Axiom. I show that the functional forms of these CSFs can be fully identified if they are characterized by a partial differential equation (PDE), which has several intuitive economic interpretations. This POE approach provides foundations for popular CSFs and their generalizations.

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Relatively little examination of the meals that are prepared in households has been conducted, despite their well-defined properties and widespread community interest in their preparation. The purpose of the present study was to identify the patterns of main meal preparation among Australian adult household meal preparers aged 44 years and younger and 45 years and over, and the relationships between these patterns and likely socio-demographic and psychological predictors. An online cross-sectional survey was conducted by Meat and Livestock Australia among a representative sample of people aged 18–65 years in Australia in 2011. A total of 1076 usable questionnaires were obtained, which included categorical information about the main meal dishes that participants had prepared during the previous 6 months along with demographic information, the presence or absence of children at home, confidence in seasonal food knowledge and personal values. Latent class analysis was applied and four types of usage patterns of thirty-three popular dishes were identified for both age groups, namely, high variety, moderate variety, high protein but low beef and low variety. The meal patterns were associated differentially with the covariates between the age groups. For example, younger women were more likely to prepare a high or moderate variety of meals than younger men, while younger people who had higher levels of education were more likely to prepare high-protein but low-beef meals. Moreover, young respondents with higher BMI were less likely to prepare meals with high protein but low beef content. Among the older age group, married people were more likely to prepare a high or moderate variety of meals than people without partners. Older people who held strong universalist values were more likely to prepare a wide variety of meals with high protein but low beef content. For both age groups, people who had children living at home and those with better seasonal food knowledge were more likely to prepare a high variety of dishes. The identification of classes of meal users would enable health communication to be tailored to improve meal patterns. Moreover, the concept of meals may be useful for health promotion, because people may find it easier to change their consumption of meals rather than individual foods.

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Part IVA of the Federal Court of Australia Act 1974 (C’th) governs the class action procedure, which has been available in Australia since March 1992. The procedure was not popular amongst the shareholders until in the late 1990s, and since then the number of shareholder class actions has steadily increased. Many of these shareholder class actions settled before a final court hearing. This article critically examines the class action procedure and in doing so, it highlights the current issues that contribute to a rapid rise in shareholder class actions. The article calls for reform to the class action procedure. It identifies areas for reform in an attempt to improve the position of the group members so that they can receive a better outcome than what they can get under the current class action model.

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Objetiva evidenciar a relevâlcia da escola enquanto lugar para o exercício de uma educação popular que se realiza como instrumento de fortalecimento da sociedade civil. Na medida em que o saber transmitido pela escola pode ser verdadeiro e assim eficaz, é que ela, de instrumento da classe dominante, tem a possibilidade de tornar-se um meio para a apropriação de conhecimento por parte dos dominados, o qual leve à consciência da própria dominação. A educação é compreendida aqui como parte do todo da sociedade e nela articulada completamente. A sociedade é ela mesma uma totalidade dinâmica cujo processo de desenvolvimento é uma continua superação-conservação de cada uma de suas etapas. O trabalho parte de uma análise das várias conjunturas politico-econômicas do Brasil, a partir dos anos 30, e das várias propostas pedagógicas que corresponderam a cada periodo, na intenção de explicitar as interações entre ambos. Encerra retomando a análise conjuntural dos anos 80 e identificando o desafio que se coloca para a educação hoje, ou se.ja: como a educação popular enquanto instrumento de fortalecimento da sociedade civil, pode contribuir para o processo de construção de uma ordem democrática dentro da sociedade brasileira.

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Dirigido e escrito por Arnaldo Jabor, Tudo bem foi veiculado nas salas de projeção no ano de 1978 - período de distensão da ditadura - com o apoio governamental da Embrafilme. O filme constitui referência documental para a análise do discurso do artista engajado, após o Golpe de 64, e a política cultural implementada pelos consecutivos governos militares. Tudo bem traz, já no título, uma ironia aos tempos vividos. A história transcorre no bairro de Copacabana, no Rio de Janeiro, num apartamento em reforma de uma família de classe média.

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To the extent that the expansion of cities is increasingly pushing and segregating the working class to outlying areas, devoid of services and infrastructure, the urban space is also important as a space in the class struggle, and in this direction, the this study aims to analyze the political organization of urban social movements and popular organizations existing in Natal-RN, nowadays, in their process of struggle for social rights, with emphasis on the right to the city. With this dimension, we appropriate the contributions of historical and dialectical materialism because we believe that this benchmark enables the understanding of the processes of collective organization and a critical perspective of totality, going beyond its immediate appearance. For production data conducted literature, documentary and field, through semi-structured interviews recorded with (the) mapped leaders of organizations in our survey, as well as advisory bodies to the movements studied. The results of the study allowed us to characterize the action of the political movements in urban Christmas struggle for recognition and guarantee of the right to the city and seize the advances and obstacles in the process of intervention of social movements and popular organizations existing in Natal, highlighting dilemmas and contradictions underlie the processes of organization and mobilization in the contemporary period. Thus, we conclude that the Natal territory, as in contemporary Brazil, the urban and political action movements that show the public scene and intertwine necessarily relate to historical trend that has been performing since the 1990s, when the country entered a period marked by a new bourgeois offensive

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Includes bibliography

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Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

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Pós-graduação em Artes - IA

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Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

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This article is a foray into the understudied issue of environmental protest politics in Central Asia. Specifically, it uses Kyrgyzstan as a case study to test the argument that environmental concerns mobilized people to engage in protest and in ways different from other kinds of protest. This essay presents the first systematic study of public opinion about the environment in Kyrgyzstan. It includes results from a 2009 nationwide survey, over 100 expert and elite interviews, and newspaper content analysis. Furthermore, it spatially analyzes these results to identify geographical variation in public perception and political event occurrence patterns. Protest engagement is a complex process determined by the interaction of several factors, and is not explained solely by affluence, rationality, or grievances. Eco-mobilization - collective political action about the environment - represents a class of protest events that offers a different view into mass discontent in the former Soviet Union and neo-patrimonial societies. The study finds that these political actions about the environment are not necessarily elite driven; there is a basic foundation of national concern and salience of these issues, and demonstrated environmental beliefs do help to explain protest behavior.

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La familia de algoritmos de Boosting son un tipo de técnicas de clasificación y regresión que han demostrado ser muy eficaces en problemas de Visión Computacional. Tal es el caso de los problemas de detección, de seguimiento o bien de reconocimiento de caras, personas, objetos deformables y acciones. El primer y más popular algoritmo de Boosting, AdaBoost, fue concebido para problemas binarios. Desde entonces, muchas han sido las propuestas que han aparecido con objeto de trasladarlo a otros dominios más generales: multiclase, multilabel, con costes, etc. Nuestro interés se centra en extender AdaBoost al terreno de la clasificación multiclase, considerándolo como un primer paso para posteriores ampliaciones. En la presente tesis proponemos dos algoritmos de Boosting para problemas multiclase basados en nuevas derivaciones del concepto margen. El primero de ellos, PIBoost, está concebido para abordar el problema descomponiéndolo en subproblemas binarios. Por un lado, usamos una codificación vectorial para representar etiquetas y, por otro, utilizamos la función de pérdida exponencial multiclase para evaluar las respuestas. Esta codificación produce un conjunto de valores margen que conllevan un rango de penalizaciones en caso de fallo y recompensas en caso de acierto. La optimización iterativa del modelo genera un proceso de Boosting asimétrico cuyos costes dependen del número de etiquetas separadas por cada clasificador débil. De este modo nuestro algoritmo de Boosting tiene en cuenta el desbalanceo debido a las clases a la hora de construir el clasificador. El resultado es un método bien fundamentado que extiende de manera canónica al AdaBoost original. El segundo algoritmo propuesto, BAdaCost, está concebido para problemas multiclase dotados de una matriz de costes. Motivados por los escasos trabajos dedicados a generalizar AdaBoost al terreno multiclase con costes, hemos propuesto un nuevo concepto de margen que, a su vez, permite derivar una función de pérdida adecuada para evaluar costes. Consideramos nuestro algoritmo como la extensión más canónica de AdaBoost para este tipo de problemas, ya que generaliza a los algoritmos SAMME, Cost-Sensitive AdaBoost y PIBoost. Por otro lado, sugerimos un simple procedimiento para calcular matrices de coste adecuadas para mejorar el rendimiento de Boosting a la hora de abordar problemas estándar y problemas con datos desbalanceados. Una serie de experimentos nos sirven para demostrar la efectividad de ambos métodos frente a otros conocidos algoritmos de Boosting multiclase en sus respectivas áreas. En dichos experimentos se usan bases de datos de referencia en el área de Machine Learning, en primer lugar para minimizar errores y en segundo lugar para minimizar costes. Además, hemos podido aplicar BAdaCost con éxito a un proceso de segmentación, un caso particular de problema con datos desbalanceados. Concluimos justificando el horizonte de futuro que encierra el marco de trabajo que presentamos, tanto por su aplicabilidad como por su flexibilidad teórica. Abstract The family of Boosting algorithms represents a type of classification and regression approach that has shown to be very effective in Computer Vision problems. Such is the case of detection, tracking and recognition of faces, people, deformable objects and actions. The first and most popular algorithm, AdaBoost, was introduced in the context of binary classification. Since then, many works have been proposed to extend it to the more general multi-class, multi-label, costsensitive, etc... domains. Our interest is centered in extending AdaBoost to two problems in the multi-class field, considering it a first step for upcoming generalizations. In this dissertation we propose two Boosting algorithms for multi-class classification based on new generalizations of the concept of margin. The first of them, PIBoost, is conceived to tackle the multi-class problem by solving many binary sub-problems. We use a vectorial codification to represent class labels and a multi-class exponential loss function to evaluate classifier responses. This representation produces a set of margin values that provide a range of penalties for failures and rewards for successes. The stagewise optimization of this model introduces an asymmetric Boosting procedure whose costs depend on the number of classes separated by each weak-learner. In this way the Boosting procedure takes into account class imbalances when building the ensemble. The resulting algorithm is a well grounded method that canonically extends the original AdaBoost. The second algorithm proposed, BAdaCost, is conceived for multi-class problems endowed with a cost matrix. Motivated by the few cost-sensitive extensions of AdaBoost to the multi-class field, we propose a new margin that, in turn, yields a new loss function appropriate for evaluating costs. Since BAdaCost generalizes SAMME, Cost-Sensitive AdaBoost and PIBoost algorithms, we consider our algorithm as a canonical extension of AdaBoost to this kind of problems. We additionally suggest a simple procedure to compute cost matrices that improve the performance of Boosting in standard and unbalanced problems. A set of experiments is carried out to demonstrate the effectiveness of both methods against other relevant Boosting algorithms in their respective areas. In the experiments we resort to benchmark data sets used in the Machine Learning community, firstly for minimizing classification errors and secondly for minimizing costs. In addition, we successfully applied BAdaCost to a segmentation task, a particular problem in presence of imbalanced data. We conclude the thesis justifying the horizon of future improvements encompassed in our framework, due to its applicability and theoretical flexibility.