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Key research themes
1. How can digital curation practices promote critical thinking and evolving professional skills?
This research area investigates the role of digital curation as both a professional activity demanding evolving skill sets and a pedagogical tool fostering critical thinking (CT) in educational contexts. Understanding this theme is pivotal as digital curation expands beyond preservation to encompass knowledge creation, evaluation, and dissemination in an increasingly digital and information-abundant environment. Insights into professionalization capture the necessary competencies and social dimensions of curation roles, while pedagogical approaches reveal how digital curation activities enhance cognitive engagement and analytical skills.
Key finding: Through a large-scale survey of self-identified digital curators, this study distinguishes between top-down professionalization (driven by expert agendas offering coherence) and bottom-up professionalization... Read more
Key finding: Using an exploratory study involving undergraduate and graduate students creating digital collections, this research empirically demonstrates that digital curation tasks engage key critical thinking components such as... Read more
Key finding: This work conceptualizes social media and Web 2.0-based digital curation as a cultural phenomenon that transforms interpersonal communication and the individual-society relationship, emphasizing the construction of personal... Read more
2. What are the challenges and opportunities in human-algorithmic collaboration for scalable and effective content curation?
Addressing the exponential growth of user-generated content, this theme explores how the interplay between human curators and algorithmic systems can be strategically balanced to enhance content curation outcomes. It investigates the limitations of fully automated curation methods, the potential of hybrid human-machine frameworks, and the sociotechnical implications of algorithmic mediation, including trust, interpretability, and agency. The research reveals how curation quality and scalability hinge on dynamic cooperation between human expertise and automated processes.
Key finding: Surveying the governance models and technological approaches for large-scale content curation and moderation, this paper identifies key challenges of exponentially growing content and community sizes, alongside ever-changing... Read more
Key finding: Examining hybrid artistic and curatorial experiments, this study problematizes the binary view of curators working either with or against algorithms. It advances a critically reflexive framework recognizing the entanglement... Read more
Key finding: Through systematic crowdsourcing experiments, the study demonstrates that machine-generated text highlighting can reduce worker cognitive load and task completion time without compromising classification accuracy,... Read more
3. What are the limitations and efficacies of alternative models to professional curation such as crowdsourcing and author-driven annotation?
This research examines pragmatic models to alleviate resource constraints in professional curation via crowdsourcing and direct author involvement, particularly in complex domains like bioinformatics. It critically analyzes empirical participation rates, quality concerns, and cost redistribution effects. The evaluation informs strategic decisions about integrating community contributions or author submissions into curated databases, balancing accuracy, efficiency, and sustainability.
Key finding: Based on two decades of empirical attempts, this perspective reveals that crowd-sourced curation suffers from consistently low participation rates across multiple projects and is generally ineffective for biological... Read more
Key finding: Through three controlled experiments with professional curators engaged in protein-protein interaction extraction, this study quantifies that natural language processing (NLP) assistance can reduce curation time by up to... Read more
Key finding: This research synthesizes collaborative and cognitive filtering techniques, sentiment analysis, and machine learning to tackle the challenges of heterogeneity and information overload inherent in user-generated content (UGC).... Read more