Development and environmental sciences: analysing thematic networks of the research agenda in Brazil
DOI:
https://doi.org/10.5380/dma.v56i0.73248Keywords:
environmental science, development, research agenda, network analysis, relational bibliometricsAbstract
The emergency of environmental concerns in the 1960s led to the comprehension of the human activity global effects on the earth system, drawing attention to the issue of development models and their multiple dimensions. The environmental sciences appeared in this scenery seeking less fragmented and more integrative approaches with a markedly interdisciplinary character. In Brazil, this field started to be constituted in the 1980s, reaching its institutionalization peak in 2011, through the creation of a new evaluation area at the post-graduation level. This paper presents the results of a study that aimed at identifying the thematic communities from the research agenda on the issue of development in Brazilian environmental sciences. The analysis focuses on the post-graduation production, mainly theses, and dissertations published between 1987 and 2017. With this purpose, the methodology included relational bibliometrics and keyword networks from the identification of 1,061 theses and dissertations that used the term development among their keywords. As a result, 41% of the theses and dissertations were found to be produced in the Northeast, showing high regional concentration. Ten thematic communities were identified, and among these the larger ones were those addressing public policies, with spatialized trimming, highlighting the rural environment and the institutional dimension, and focus on tourism as a local development agent. The other communities identified were economy-technology, socioenvironmental-impact, agriculture-family, indicator-water, health-human, residue-solid, fishing-industry, and credit-clear. Therefore, thematic diversity is shown as an important component in the debate over development in environmental sciences, and at the same time, relational bibliometrics and word network analysis have their potential highlighted concerning the understanding of science production from large document collections.
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