DIGITAL INCLUSION AND THE DEMOCRATIZATION OF MACHINE LEARNING:
A BIBLIOMETRIC STUDY ON LOW-CODE/NO-CODE TOOLS IN THE FORMATION OF CITIZEN DEVELOPERS
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https://doi.org/10.56579/rei.v7i4.1572Keywords:
Bibliometric Analysis, Low-code, No-code, Machine learning, Citizen developmentsAbstract
In recent years, low/no-code tools have gained prominence as a response to the shortage of qualified professionals in the face of growing demand for innovative digital solutions. This study conducts a bibliometric analysis to investigate how these tools, when applied to machine learning, have been integrated into initiatives aimed at training citizen developers. This approach makes it possible to identify patterns, trends, and gaps in the literature, offering a structured view of the thematic evolution of the field. A total of 351 documents from the Scopus database, published between 2014 and 2024, were analyzed using four bibliometric techniques: (1) performance analysis to measure quantitative indicators; (2) keyword co-occurrence to map core concepts; (3) co-citation to identify the theoretical foundation of the field; and (4) bibliographic coupling to highlight emerging research fronts. The results indicate growth in scientific production, with a peak in 2023, signaling a strengthening of academic interest in the use of low/no-code tools as a strategy to democratize machine learning. The analysis reinforces the importance of these platforms in the digital transformation and in including non-technical individuals in the development of advanced solutions.
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