This article explores how evolving scholarly works can be represented differently in bibliographic databases. Using F1000Research as an example, it explains how multiple published versions of the same scholarly contribution can create metadata instability, publication divergence, and citation divergence, with potential implications for publication counts and citation-based indicators.
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An organisation can keep humans in the loop and still lose the expertise that makes their oversight meaningful. When AI analyses evidence, frames options and drafts recommendations, it can also displace the work through which experts develop and sustain judgement. People may retain the authority to approve a decision while becoming less able to reconstruct how it was reached, challenge its assumptions or exercise independent judgement. For leaders, the question is not simply whether a human signs off. It is whether the organisation is preserving the expertise that makes that sign-off worth having.
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Interdisciplinary studies highlight the value of information in diverse contexts, such as business, disasters and risk management, health, and migration. The importance of information in various contexts is well established. However, our study of one of the most vulnerable communities in Bangladesh, street-based female sex workers (FSWs), revealed that “information” alone may not always be sufficient to reduce HIV/AIDS and STI prevalence, especially when some marginalized communities are unable to practice safer sexual practices due to several factors, including stigma associated with their profession. The study also demonstrated FSWs’ vulnerability in accessing information.
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Every reference in a research paper is a promise. It tells readers: if you want to check this claim, here is where the evidence came from. References allow researchers to trace ideas, verify findings, and understand how new discoveries build on earlier work. But what happens when a citation leads nowhere?
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A search system can prioritize papers that are very closely related to a query, producing a narrow and highly focused set of results. Or it can broaden the view, including more diverse but still relevant work that may point in new directions. Most systems, however, make this trade-off internally and invisibly. This raises a simple but important question: what if researchers could influence this balance themselves, instead of accepting a fixed ranking?
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Have you ever searched online for a health symptom late at night and ended up reading long discussion threads from strangers on Reddit or Quora? Maybe you were looking for reassurance, wondering whether someone else had experienced the same thing, or simply trying to avoid another stressful trip to the doctor. For millions of people, online Q&A platforms have quietly become part of everyday health information seeking.
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Young and old, those seeking learning opportunities beyond traditional paths find Open Distance Learning (ODL) opportunities as their best resort. Distance learning open doors for a vast and diverse community to education yet leaves the student in isolation. Isolation in ODL is the physical and psychological disconnection a student experience from their learning community. It leads to increased emotional and academic stress resulting in high dropout rates.
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During my years working as a public librarian, I regularly facilitated reading club meetings. I observed that the conversations that emerged when people gathered around the same book involved much more than the book itself. Sometimes childhood memories were recalled through the protagonist of a novel; at other times, a poem opened the door to personal experiences. By the end of these meetings, it was evident that participants had come to know not only a book but also one another more closely.
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Citizens act as co-creators who should collaborate with archivists to enrich AI-generated archival descriptions, by highlighting the symbolic and emotional aspects conveyed by these records. This role is complemented by that of citizens who serve as auditors, verifying the quality of the outputs generated by disruptive technologies.
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For decades, discussions about international scientific collaboration have focused on questions such as: How many countries participate? How large are research teams? How diverse are collaborations? But a more fundamental question remains underexplored: Who is studying whom?
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