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This is an attempt to introduce proactive changes when creating and providing intellectual access in order to convince catalogers to become more social catalogers then they have ever been in the past.

Through a brief review and analysis of relevant literature a definition of social cataloging and social cataloger is given.

User contributed content to library catalogs affords informational professionals the opportunity to see directly the users’ perceptions of the usefulness and about-ness of information resources. This is a form of social cataloging especially from the perspective of the information professional seeking to organize information to support knowledge discovery and access.

The user and the cataloger exercise their voice as to what the information resources are about, which in essence is interpreting the intentions of the creator of the resources, how the resource is related to other resources, and perhaps even how the resources can be, or have been, used. Depending on the type of library and information environment, the weight of the work may or may not fall equally on both user and cataloger.

New definitions of social cataloging and social cataloguing are offered and are linked back to Jesse Shera’s idea of social epistemology.

Collaborative forms range from co-located teams engaged in short term local projects, to international joint ventures, to worldwide networks of organizations and citizens linked together to generate global social change. In order to discern patterns that transcend the breadth of forms (including virtual), a new term is introduced that encompasses the entire spectrum: collaborative entity (CE). The diverse and far-ranging CE literature is then integrated into the Collaborative Capacity (CC) Framework. That framework is comprised of ten broad constructs and their interrelationships that, when considered together, capture fundamental aspects of all CEs. The CC Framework provides a bridge-building language to help facilitate inter-disciplinary, multi-dimensional dialogue, research, and perspectives on fostering collaborative capacity.

This chapter aims to further the conceptual clarity of co-creation, by classifying and exploring the spectrum of non-government actor–led governance arrangements for the co-creation of nature-based solutions (NBS) across different European contexts. Case studies from pilot demonstrators in current Horizon 2020 projects (proGIreg, CLEVER Cities, and EdiCitNet in Italy, Germany, the Netherlands, and the United Kingdom) are used to illustrate collaborative governance arrangements within the operating space of co-creation, delineate respective actor roles, and identify lessons learnt.

This chapter describes how conversational computer agents have been used in collaborative problem-solving environments. These agent-based systems are designed to (a) assess the students’ knowledge, skills, actions, and various other psychological states on the basis of the students’ actions and the conversational interactions, (b) generate discourse moves that are sensitive to the psychological states and the problem states, and (c) advance a solution to the problem. We describe how this was accomplished in the Programme for International Student Assessment (PISA) for Collaborative Problem Solving (CPS) in 2015. In the PISA CPS 2015 assessment, a single human test taker (15-year-old student) interacts with one, two, or three agents that stage a series of assessment episodes. This chapter proposes that this PISA framework could be extended to accommodate more open-ended natural language interaction for those languages that have developed technologies for automated computational linguistics and discourse. Two examples support this suggestion, with associated relevant empirical support. First, there is AutoTutor, an agent that collaboratively helps the student answer difficult questions and solve problems. Second, there is CPS in the context of a multi-party simulation called Land Science in which the system tracks progress and knowledge states of small groups of 3–4 students. Human mentors or computer agents prompt them to perform actions and exchange open-ended chat in a collaborative learning and problem-solving environment.

Reciprocal partnerships between institutions of higher education (IHEs) and communities provide opportunities for IHEs to fulfill their core mission while at the same time benefiting communities. One model of institutional accountability for this type of partnership is the Elective Carnegie Community Engagement (CE) Classification. As a process is underway to internationalize the US-based classification, this chapter engages with a central guiding question: How can we best adapt the CE classification’s institutionalizing framework for CE – designed in the context of the United States – in a way that upholds the integrity of engagement practices, adheres to effective strategies for organizational change, and is sensitive to national, cultural, economic, political, social, and historical contexts? In addressing this question, the internationalization strategy is focused on careful adaptation of the application framework so that it can be applied in specific national higher education contexts. The adaptation seeks to incorporate nationally and culturally relevant CE approaches that are reflected in organizational strategies at the institutional level, consistent with the internal logic of the CE classification: valuing expertise of others, working against colonial knowledge regimes, and mindfully building toward increased epistemic justice. This strategy can be a model for internationalization of other processes for IHEs.

The increasing use of digital technologies in organizational contexts, like collaborative social platforms, has not only changed the way people work but also provided organizations with new and wide ranges of data sources that could be analyzed to enhance organizational- and individual-level outcomes, especially when integrated with more traditional tools. In this study, we explore the relationship between data flows generated by employees on companies’ digital environments and employees’ attitudes measured through surveys. In a sample of 107 employees, we collected data on the number and types of actions performed on the company’s digital collaborative platform over a two-year period and the level of organizational embeddedness (fit, sacrifice, and links dimensions) through two rounds of surveys over the same period. The correlation of the quantity and quality of digital actions with the variation of organizational embeddedness over the same period shows that workers who engaged in more activities on the digital platform also experienced an increase in their level of organizational embeddedness mainly in the fit dimension. In addition, the higher the positive variation of fit, the more employees performed both active and passive digital actions. Finally, the higher the variation of organizational embeddedness, the more employees performed networking digital behaviors.

Team cognition research continues to evolve as the need for understanding and improving complex problem solving itself grows. Complex problem solving requires members to engage in a number of complicated collaborative processes to generate solutions. This chapter illustrates how the Macrocognition in Teams model, developed to guide research on these processes, can be utilized to propose how intelligent tutoring systems (ITSs) could be developed to train collaborative problem solving. Metacognitive prompting, based upon macrocognitive processes, was offered as an intervention to scaffold learning these complex processes. Our objective is to provide a theoretically grounded approach for linking intelligent tutoring research and development with team cognition. In this way, team members are more likely to learn how to identify and integrate relevant knowledge, as well as plan, monitor, and reflect on their problem-solving performance as it evolves. We argue that ITSs that utilize metacognitive prompting that promotes team planning during the preparation stage, team knowledge building during the execution stage, and team reflexivity and team knowledge sharing interventions during the reflection stage can improve collaborative problem solving.

This chapter aims to discuss the issues associated with social indexing as a solution to the challenges of current information organization systems by investigating the quality and efficacy of social indexing.

The chapter focuses on the study which compared indexing similarity between two professional groups and also compared social tagging and professional indexing. The study employed the method of the modified vector-based Indexing Consistency Density (ICD) with three different similarity measures: cosine similarity, dot product similarity, and Euclidean distance metric.

The investigation of social indexing in comparison of professional indexing demonstrates that social tags are more accurate descriptions of resources and reflection of more current terminology than controlled vocabulary. Through the characteristics of social tagging discussed in this chapter, we have a clearer understanding of the extent to which social indexing can be used to replace and improve upon professional indexing.

As investment in professionally developed web directories diminishes, it becomes even more critical to understand the characteristics of social tagging and to obtain benefit from it. In future research, the examination of subjective tags needs to be conducted. A survey or user study on tagging behavior also would help to extend understanding of social indexing practices.