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Information Science Breaking up?

Many would agree that the institutional library/information service R&D, information seeking, information retrieval, and biblio/infor/scientometrics form the landscape of Library and Information Science (LIS). These subareas are also brought forward in introductions to, and analyses of LIS (e.g., curricula, lectures, textbooks). Still, scholars contributing to different subareas may increasingly turn their attention toward the territory outside LIS – and be integrated there – or remain as visitors to LIS, being based outside. Are there indications toward that in LIS research? We decided to analyze the trends.

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ChatGPT and AI-Written Research Papers: Ethical Considerations for Scholarly Publishing

Our study reveals that the potential for bias within AI-driven language models, such as GPT-3, poses a significant threat to the integrity of science. These models are trained on vast amounts of data, primarily from the internet, which can lead to a bias in the data. For example, if the data source is biased or incomplete, this bias will be reflected in the model’s output.

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