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 GroganzOpenSourceBR2011 Aug.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:Benefits of the community for partners of open source vendors|Benefits of the community for partners of open source vendors]]"'''
'''"[[Journal:Judgements of research co-created by generative AI: Experimental evidence|Judgements of research co-created by generative AI: Experimental evidence]]"'''


Open source vendors can benefit from business ecosystems that form around their products. Partners of such vendors can utilize this ecosystem for their own business benefit by understanding the structure of the ecosystem, the key actors and their relationships, and the main levers of profitability. This article provides [[information]] on all of these aspects and identifies common business scenarios for partners of open source vendors. Armed with this information, partners can select a strategy that allows them to participate in the ecosystem while also maximizing their gains and driving adoption of their product or solution in the marketplace.
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 />
 
''Recently featured'':
Every [[Free and open-source software#FLOSS|free/libre open source software]] (F/LOSS) vendor strives to create a business ecosystem around its software product. Doing this offers two primary advantages from a sales and marketing perspective: i) it increases the viability and longevity of the product in both commercial and communal spaces, and ii) it opens up new channels for communication and innovation. ('''[[Journal:Benefits of the community for partners of open source vendors|Full article...]]''')<br />
{{flowlist |
 
* [[Journal:Geochemical biodegraded oil classification using a machine learning approach|Geochemical biodegraded oil classification using a machine learning approach]]
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* [[Journal:Knowledge of internal quality control for laboratory tests among laboratory personnel working in a biochemistry department of a tertiary care center: A descriptive cross-sectional study|Knowledge of internal quality control for laboratory tests among laboratory personnel working in a biochemistry department of a tertiary care center: A descriptive cross-sectional study]]
''Recently featured'': [[Journal:adLIMS: A customized open source software that allows bridging clinical and basic molecular research studies|adLIMS: A customized open source software that allows bridging clinical and basic molecular research studies]], [[Journal:MendeLIMS: A web-based laboratory information management system for clinical genome sequencing|MendeLIMS: A web-based laboratory information management system for clinical genome sequencing]], [[Journal:Personalized Oncology Suite: Integrating next-generation sequencing data and whole-slide bioimages|Personalized Oncology Suite: Integrating next-generation sequencing data and whole-slide bioimages]]
* [[Journal:Sigma metrics as a valuable tool for effective analytical performance and quality control planning in the clinical laboratory: A retrospective study|Sigma metrics as a valuable tool for effective analytical performance and quality control planning in the clinical laboratory: A retrospective study]]
}}

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...)
Recently featured: