O que a Inteligência Artificial “pensa” sobre a avalição da pesquisa – em Inglês

Resolvi fazer um teste com o ChatGPT https://chat.openai.com/ sobre a avaliação da pesquisa. Olhem o resultado:

Bibliometric evaluation, or the use of quantitative measures of research output and impact, is a common method for evaluating researchers and their work. This can include measures such as the number of papers published, the number of citations a paper receives, and the impact factor of the journals in which the papers are published.


However, while bibliometric evaluation can provide valuable information about the quantity and reach of a researcher’s work, it has limitations when it comes to evaluating the quality or value of that work. This is because bibliometric measures are based on the number of times a paper is cited or published, and do not necessarily reflect the substance or originality of the research.


For example, a paper that is highly cited may have had a significant impact on a particular field, but this does not necessarily mean that it is of high quality or that it represents the best work produced by the researcher. Similarly, a researcher who has published a large number of papers may not necessarily be producing high-quality work.


In contrast to bibliometric evaluation, real production quality evaluation involves a more in-depth analysis of the substance and value of a researcher’s work. This could involve reviewing the research methods used, the originality and significance of the findings, and the relevance and impact of the work in the broader context of the field.


In order to accurately assess the quality of a researcher’s work, it is important to consider both bibliometric and real production quality evaluations. Bibliometric measures can provide valuable information about the reach and impact of a researcher’s work, while real production quality evaluation can provide a more nuanced understanding of the substance and value of that work.