Motivação e docência no Esino Especial: um estudo com professoras do Distrito Federal

The study of motivation at work is a recurring theme, but little explored in relation to teacher motivation and to a lesser extent also in a work context as peculiar as special education. Considering the social relevance of teaching this type of education and its specificities, this study aimed to investigate the motivation and their conditioning factors of special education teachers. Participated research 33 special education teachers from public schools of the Federal District, responding to measure Motivation at Work, composed of sections that evaluate three components of motivation dimensions to work: Section I evaluating the valence (which is valued by the individual); Section II directed the evaluation of instrumentality (perception of work as a way to reach your goals); and Section III measuring the Expectancy (perception of how your efforts are rewarded by the current job). Motivation of the General scores obtained for the sample showed a level of motivation considered high ( = 89,36), but the individual scores indicated a significant variation ( = 20,70; s2 = 428,50), and when grouped participants per level was obtained 60.61% of high scores; 36.36% of mediated scores and 3.03% of low scores. A detailed analysis of the scores for sections and items of the instrument indicated that high levels of motivation of the participants suffer negative influence of working conditions. The evaluation of items related to autonomy, relationship with managers and participation in organizational decisions, had greater potential for reducing the overall motivation of these professionals.O estudo da motivação no trabalho é tema recorrente, mas pouco explorado em relação a motivação do professor e, em menor grau ainda, em um contexto laboral tão peculiar quanto a educação especial. Tendo em vista a relevância social do trabalho docente nesta modalidade de ensino e suas especificidades, este estudo objetivou a investigação da motivação e seus fatores condicionantes de docentes do ensino especial. Participaram pesquisa 33 professoras de ensino especial da rede pública do Distrito Federal, respondendo a Medida de Motivação no Trabalho, composta por seções que avaliam três dimensões componentes da motivação para trabalhar: Seção I avaliando a valência (o que é valorizado pelo indivíduo); Seção II direcionada a avaliação da instrumentalidade (percepção do trabalho como forma de alcançar seus objetivos); e Seção III mensurando a expectância (percepção de como seus esforços são recompensados pelo trabalho atual). Os Escores Gerais de Motivação obtidos para a amostra apresentaram um nível de motivação considerado alto (x= 89,36), entretanto os escores individuais indicaram uma variação significativa (s = 20,70; s2 = 428,50), e quando agrupadas as participantes por nível obteve-se 60,61% de escores altos; 36,36% de escores mediados e 3,03% de escores baixos.  A análise detalhada dos escores por seções e itens do instrumento indicou que os altos níveis de motivação das participantes sofre influência negativa das condições de trabalho. A avaliação dos itens relacionados a autonomia, a relação com chefias e a participação nas decisões da organização, apresentaram maior potencial de diminuição da motivação global destas profissionais

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