Scholarly Publishing - Information Matters

In my previous career as a systems librarian, I saw information privilege as a technical problem: who has the password to the database, and who has the funding for the subscription? But as I transitioned into research with Chinese immigrant cancer survivors, I realized that privilege runs deeper than a personal login. Even if we made every medical journal in the world “Open Access” tomorrow, many of the survivors I work with would still be excluded from the conversation. Removing a paywall doesn’t help if the person behind it can’t read the language or can’t find their cultural realities reflected in what counts as evidence.

Original

February 4, 2026 Pathum B Rathnayake

Open Access Boom: Citations Up, Barriers Up

Open Access (OA) publishing has become a major change in how research is shared. In traditional publishing, readers pay to access articles through journal subscriptions. In OA, articles are free to read online right away. This trend has grown fast because many believe it makes science better and fairer. Supporters say OA articles get more citations because more people can read them, leading to greater impact. However, authors often pay high Article Processing Charges (APCs) to publish in OA journals. This creates a debate: Does OA truly boost research impact, or does it create new problems by charging authors large fees?

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February 4, 2026 Rebecca D. Frank

The Transparency Gap: What’s Missing from Qualitative Research Reporting in Information Science?

How do Information Science researchers describe their use of qualitative methods? What do they say about their approach to different steps in the research process such as dates of data collection, people involved in the research process, and whether they’ve obtained ethics board approval for their work? What information gets left out? These questions lie at the heart of ongoing conversations around trust in research and the reusability of research data across the academic landscape.

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March 15, 2023 Brady Lund

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.