53 research outputs found
La práctica profesional de los agentes sociales en materia de ocio juvenil: estrategias para la intervención
Basándose en una muestra de treinta y cuatro (34) agentes sociales, este artículo
analiza su práctica profesional en materia de ocio juvenil, a nivel nacional. Se incide en
las estrategias utilizadas en la intervención, a saber: objetivos; metodología y coordinación
inter e intra institucional; fuentes de financiación; estrategias de comunicación y difusión; y
sistema de evaluación. Ante ello, se afrontan como principales objetivos identificar la práctica
profesional en materia de ocio juvenil desde la perspectiva de los agentes sociales y definir
indicadores que sean el punto de partida para identificar buenas prácticas en este campo.
Se aborda un marco metodológico centrado en la investigación evaluativa de carácter
diagnóstica, exploratoria y descriptiva. En este contexto, se configura un estudio piloto, cuyas
técnicas de recogida de datos sobre la práctica profesional de los encuestados han sido en
primer lugar, el diseño de un cuestionario abierto, seguido de un segundo cuestionario cerrado
que ha partido del análisis de contenido de las respuestas dadas al primero, con el fin de
identificar indicadores comunes de la práctica profesional y así poder establecer un patrón
de referencia que pueda validarse desde la misma.
Finalmente se identifican ocho indicadores clave como referentes para desarrollar una
intervención de calidad en materia de ocio juvenil, desde el trabajo y las valoraciones de los
expertos consultados.info:eu-repo/semantics/publishedVersio
Holistic face processing is penetrable … depending on the composite design
Holistic processing (HP) of faces is usually measured by the composite effect. While Weston and Perfect [2005. Effects of processing bias on the recognition of composite face halves. Psychonomic Bulletin & Review, 12, 1038–1042. doi:10.3758/BF03206440] found that priming at the local level speeded recognition of components of faces, Gao et al. [2011. Priming global and local processing of composite faces: Revisiting the processing-bias effect on face perception. Attention Perception & Psychophysics, 73, 1477–1486. doi:10.3758/s13414-011-0109-7] found that only global priming had an effect on HP of faces. The two studies used different versions of the composite task (the partial design, which is considered to be prone on bias, and the complete design). However, the two studies also differed in other respects and it is difficult to know to what extent issues with the partial design contributed to the differing conclusions. In the present study, the HP indexed by the complete design measure was augmented by global priming. In contrast, no effect was observed in the partial design index. We claim that the partial design index reflects other factors besides HP, including response bias, and conclude that HP can be understood within the context of domain-general attentional processes
O Eixo Atlántico: un territorio educador, unha comunidade educativa
Eixo Atlântico do Noroeste PeninsularEixo Atlântico do Noroeste Peninsularinfo:eu-repo/semantics/publishedVersio
Hemispheric asymmetry in holistic processing of words
Holistic processing has been regarded as a hallmark of face perception, indicating the automatic and obligatory tendency of the visual system to process all face parts as a perceptual unit rather than in isolation. Studies involving lateralized stimulus presentation suggest that the right hemisphere dominates holistic face processing. Holistic processing can also be shown with other categories such as words and thus it is not specific to faces or face-like expertize. Here, we used divided visual field presentation to investigate the possibly different contributions of the two hemispheres for holistic word processing. Observers performed same/different judgment on the cued parts of two sequentially presented words in the complete composite paradigm. Our data indicate a right hemisphere specialization for holistic word processing. Thus, these markers of expert object recognition are domain general
A list of land plants of Parque Nacional do Caparaó, Brazil, highlights the presence of sampling gaps within this protected area
Brazilian protected areas are essential for plant conservation in the Atlantic Forest domain, one of the 36 global biodiversity hotspots. A major challenge for improving conservation actions is to know the plant richness, protected by these areas. Online databases offer an accessible way to build plant species lists and to provide relevant information about biodiversity. A list of land plants of “Parque Nacional do Caparaó” (PNC) was previously built using online databases and published on the website "Catálogo de Plantas das Unidades de Conservação do Brasil." Here, we provide and discuss additional information about plant species richness, endemism and conservation in the PNC that could not be included in the List. We documented 1,791 species of land plants as occurring in PNC, of which 63 are cited as threatened (CR, EN or VU) by the Brazilian National Red List, seven as data deficient (DD) and five as priorities for conservation. Fifity-one species were possible new ocurrences for ES and MG states
Pervasive gaps in Amazonian ecological research
Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear un derstanding of how ecological communities respond to environmental change across time and space.3,4
While the increasing availability of global databases on ecological communities has advanced our knowledge
of biodiversity sensitivity to environmental changes,5–7 vast areas of the tropics remain understudied.8–11 In
the American tropics, Amazonia stands out as the world’s most diverse rainforest and the primary source of
Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepre sented in biodiversity databases.13–15 To worsen this situation, human-induced modifications16,17 may elim inate pieces of the Amazon’s biodiversity puzzle before we can use them to understand how ecological com munities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus
crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced
environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple or ganism groups in a machine learning model framework to map the research probability across the Brazilian
Amazonia, while identifying the region’s vulnerability to environmental change. 15%–18% of the most ne glected areas in ecological research are expected to experience severe climate or land use changes by
2050. This means that unless we take immediate action, we will not be able to establish their current status,
much less monitor how it is changing and what is being lostinfo:eu-repo/semantics/publishedVersio
Pervasive gaps in Amazonian ecological research
Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost
Pervasive gaps in Amazonian ecological research
Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost
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