A pedagogia da alternancia e suas possibilidades para os estudantes do campo: um estudo de caso sobre a escola família agrícola de Sobradinho-BA / The pedagogy of alternancy and its possibilities for students in the field: a case study about the escola família agrícola of Sobradinho-BA

No presente estudo pretendemos discutir sobre como a pedagogia da alternância, veiculada a uma base curricular diversificada, tem sido mobilizada pela Escola Família Agrícola de Sobradinho (EFAS), para formação de estudantes oriundos das comunidades rurais sem limitar suas perspectivas de futuro a vida no campo. As novas possibilidades que se desnudam para esses estudantes nuançam os estereótipos construídos mediante os pressupostos de uma Sociologia com fundamentos durkheimiano, que associa a esses indivíduos os mesmos critérios aplicados para categorizar o campo, opondo-o a cidade. Os dados dispostos no corpo do texto foram colhidos a partir de técnicas consagradas pelas pesquisas qualitativas, assim, durante um mês imergimos no cotidiano da mencionada escola, o que nos possibilitou tecer relações de confiança com os estudantes e posteriormente realizar entrevistas seguindo roteiros semiestruturados. Concluímos ponderando que a formação diversificada proposta pela escola, organizada a partir da pedagogia da alternância tem contribuído para ampliação dos horizontes existências dos estudantes, possibilitando-lhes construir perspectivas de vida diversas, tanto no campo, quanto na cidade. Perspectivas que foram expressas nas falas dos entrevistados, que revelaram anseios variados, assim, enquanto o primeiro deseja retornar a sua comunidade para contribuir com o desenvolvimento sustentável da mesma, o segundo pretende trabalhar como analista de sistemas de uma grande empresa na cidade, ao passo que a terceira entrevistada projeta desenvolver atividades tanto no campo, quanto na cidade. 

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