Difference between revisions of "Template:Article of the week"

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<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Fig1_Joyce_2015.png|220px]]</div>
<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Fig1 Niszczota EconBusRev23 9-2.png|240px]]</div>
'''"[[Journal:Generalized procedure for screening free software and open-source software applications|Generalized procedure for screening free software and open-source software applications]]"'''
'''"[[Journal:Judgements of research co-created by generative AI: Experimental evidence|Judgements of research co-created by generative AI: Experimental evidence]]"'''


Free software and [[:Category:Open-source software|open-source software projects]] have become a popular alternative tool in both scientific research and other fields. However, selecting the optimal application for use in a project can be a major task in itself, as the list of potential applications must first be identified and screened to determine promising candidates before an in-depth analysis of systems can be performed. To simplify this process, we have initiated a project to generate a library of in-depth reviews of free software and open-source software applications. Preliminary to beginning this project, a review of evaluation methods available in the literature was performed. As we found no one method that stood out, we synthesized a general procedure using a variety of available sources for screening a designated class of applications to determine which ones to evaluate in more depth. In this paper, we examine a number of currently published processes to identify their strengths and weaknesses. By selecting from these processes we synthesize a proposed screening procedure to triage available systems and identify those most promising of pursuit. ('''[[Journal:Generalized procedure for screening free software and open-source software applications|Full article...]]''')<br />
The introduction of [[ChatGPT]] has fuelled a public debate on the appropriateness of using generative [[artificial intelligence]] (AI) ([[large language model]]s or LLMs) in work, including a debate on how they might be used (and abused) by researchers. In the current work, we test whether delegating parts of the research process to LLMs leads people to distrust researchers and devalues their scientific work. Participants (''N'' = 402) considered a researcher who delegates elements of the research process to a PhD student or LLM and rated three aspects of such delegation. Firstly, they rated whether it is morally appropriate to do so. Secondly, they judged whether—after deciding to delegate the research process—they would trust the scientist (who decided to delegate) to oversee future projects ... ('''[[Journal:Judgements of research co-created by generative AI: Experimental evidence|Full article...]]''')<br />
 
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Latest revision as of 15:26, 20 May 2024

Fig1 Niszczota EconBusRev23 9-2.png

"Judgements of research co-created by generative AI: Experimental evidence"

The introduction of ChatGPT has fuelled a public debate on the appropriateness of using generative artificial intelligence (AI) (large language models or LLMs) in work, including a debate on how they might be used (and abused) by researchers. In the current work, we test whether delegating parts of the research process to LLMs leads people to distrust researchers and devalues their scientific work. Participants (N = 402) considered a researcher who delegates elements of the research process to a PhD student or LLM and rated three aspects of such delegation. Firstly, they rated whether it is morally appropriate to do so. Secondly, they judged whether—after deciding to delegate the research process—they would trust the scientist (who decided to delegate) to oversee future projects ... (Full article...)
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