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User‐created metadata, often referred to as folksonomy or social classification, has received a considerable amount of attention in the digital library world. Social tagging is perceived as a tool for enhancing description of digital objects and providing a venue for user input and greater user engagement. This article seeks to examine the pros and cons of user‐generated metadata in the context of digital image collections and compares it to professionally created metadata schema and controlled vocabulary tools.

The article provides an overview of challenges to concept‐based image indexing. It analyzes the characteristics of social classification and compares images described by users to a set of images indexed in a digital collection.

The article finds that user‐generated metadata vary in the level of description, accuracy, and consistency and do not provide a solution to the challenges of image indexing. On the other hand, they reflects user's language and can lead toward user‐centered indexing and greater user engagement.

Social tagging can be implemented as a supplement to professionally created metadata records to provide an opportunity for users to comment on images.

The article introduces the idea of user‐centered image indexing in digital collections.

A welcome sign of the growing recognition of the need for more attention to research and documentation in the social sciences can be found in the Report of the Committee on Social Studies under the chairmanship of Lord Heyworth. Not that the report has a great deal to say on the documentation aspect; it has a few fairly cordial references to libraries, and a paragraph mentioning their inadequacies, with a recommendation that national lending facilities should be provided. But the committee were evidently aware of the need for better collection and dissemination of information, and regarded this as one of the major tasks of the Social Science Research Council, of which Dr Michael Young, Director of the Institute for Community Studies, has now been appointed chairman.

This paper aims to sets out to provide encouragement to new researchers and challenge the widespread belief that in order to get published you need to travel on the coat tails of a Big Name in accounting research. With this in mind, the argument is that a key contribution of Qualitative Research in Accounting & Management (QRAM) has been to provide such encouragement.

The approach is conceptual and theoretical employing the theories of Bourdieu on social dominance and status, and the work of Plantinga on the nature of perception and belief.

These take the form of an argument that all social fields are structured so that levels of dominance are inevitable given the disposition (or habitus) of actors in the field to be so structured. Thus, the structure awards capital, in the form of status, while denying capital to others. Coupled with this human tendency to form structured fields is a reasoning that is inductive, and, as Plantinga points out, based on underlying beliefs and perceptions.

Hopefully, this paper will prove controversial with many denying that in the liberal environment of accounting research something less than an egalitarian meritocracy could possibly exist. It is piously hoped, however, that among “lowly” researchers the paper will strike a chord that resonates.

This chapter discusses and interprets examples of safe spaces through the lenses provided in Chapters 2 and 3. Specifically, we discuss a few diverse examples of safe spaces for learning and development taken from children's literature, an art exhibit, a feature-length movie, and a professional development experience, detailing how each can be seen in terms of to what extent it offers a separate safe space, works with aspects of sameness/difference and intersectionality, and/or creates a space for democratic iterations that address one or more of the levels of inequity.

This paper aims to focus on how home-grown Indian companies explored the potential of Indian middle class and realized an opportunity to seize the market gap not catered by MNCs in India. Across three distinct business contexts, the authors describe the companies’ procedures of developing segment-specific offerings. Doing so, the authors outline novel strategies implemented by these companies to cater to specific needs of the segments.

Seizing Bandura’s (1986) framework that stresses on the role of cognitive, vicarious, self-reflective and self-regulatory processes, the authors develop a four-layered model of the Indian middle class consumers. Building upon this model, they took multiple case (three caselets) approach for illustrating the strategies of home-grown companies. The authors identify their potential to explore the unknown terrains of various market segments and rework with unique local solutions.

The study highlights the power of home-grown companies over MNCs in terms of better market understanding and realistic offerings best suited to their needs. Across the divergent business contexts the companies’ strategies have four features in common: customer targeting and developing; localization of business models, particularly services; relating the products to the Indian society; and ethnocentrism and pride.

This study gives priority to a “thick” description of the proceedings without claiming causality. The authors limit this qualitative investigation to pinpointing congruence and contradictions to previous established results.

A key implication of this paper is the relevance of linking firm’s strategy to social-psychological development of customers in emerging economies component. This study provides critical insights for both managers and policymakers on the economic and social upswing as socially responsible and ethical practices are likely to gain public awareness.

The study’s originality springs from understanding the domestic company’s strategies when facing the pressure of (mainly Western) MNCs entering the emerging economies markets. While the latter takes advantage of economies of scale, country of origin effects and the powerful brands, the home-grown businesses are forced to develop divergent advantages and capabilities. Notably, earlier literature focused on changed demand pattern brought by MNCs in emerging economies and not on later part whereby, home-grown companies carve a space for themselves with specially designed improved products and innovative strategies.

Examines one of the most important reforms relating to public enterprise (PE) policy in India, namely divestment of their share‐holdings. Discusses the philosophy, process, organizational mechanism, expectations and outcomes of divestment in PEs. Finally, points out the major weaknesses retarding the success of the newly introduced divestment policy and outlines some reformatory measures to overcome them. As a backdrop, presents the historical background, current scenario, and problems and performance of PEs in India, but has been restricted to the central PEs, i.e. enterprises owned and managed by the central government only.

One major aspect of T.D. Wilson’s research has been his insistence on situating the investigation of information behaviour within the context of its occurrence Ö within the everyday world of work. The significance of this approach is reviewed in light of the notion of embodied cognition that characterises the evolving theoretical episteme in cognitive science research. Embodied cognition employs complex external props such as stigmergic structures and cognitive scaffoldings to reduce the cognitive burden on the individual and to augment human problem‐solving activities. The cognitive function of the classification scheme is described as exemplifying both stigmergic structures and cognitive scaffoldings. Two different but complementary approaches to the investigation of situated cognition are presented: cognition‐as‐scaffolding and cognition‐as‐infrastructure. Classification‐as‐scaffolding views the classification scheme as a knowledge storage device supporting and promoting cognitive economy. Classification‐as‐infrastructure views the classification system as a social convention that, when integrated with technological structures and organisational practices, supports knowledge management work. Both approaches are shown to build upon and extend Wilson’s contention that research is most productive when it attends to the social and organisational contexts of cognitive activity by focusing on the everyday world of work.

Classification is an important process in making sense of the world, and has a pronounced social dimension. This paper aims to compare folksonomy, a new social classification system currently being developed on the web, with conventional taxonomy in the light of theoretical sociological and anthropological approaches. The co‐existence of these two types of classification system raises the questions: Will and should taxonomies be hybridized with folksonomies? What can each of these systems contribute to information‐searching processes, and how can the sociology of knowledge provide an answer to these questions? This paper aims also to address these issues.

This paper is situated at the meeting point of the sociology of knowledge, epistemology and information science and aims at examining systems of classification in the light of both classical theory and current late‐modern sociological and anthropological approaches.

Using theoretical approaches current in the sociology of science and knowledge, the paper envisages two divergent possible outcomes.

While concentrating on classifications systems, this paper addresses the more general social issue of what we know and how it is known. The concept of hybrid knowledge is suggested in order to illuminate the epistemological basis of late‐modern knowledge being constructed by hybridizing contradictory modern knowledge categories, such as the subjective with the objective and the social with the natural. Integrating tree‐like taxonomies with folksonomies or, in other words, generating a naturalized structural order of objective relations with social, subjective classification systems, can create a vast range of hybrid knowledge.

This paper aims to discuss how collaborative classification works in online music information retrieval systems and its impacts on the construction, fixation and orientation of the social uses of popular music on the internet.

Using a comparative method, the paper examines the logic behind music classification in Recommender Systems by studying the case of Last.fm, one of the most popular web sites of this type on the web. Data collected about users' ritual classifications are compared with the classification used by the music industry, represented by the AllMusic web site.

The paper identifies the differences between the criteria used for the collaborative classification of popular music, which is defined by users, and the traditional standards of commercial classification, used by the cultural industries, and discusses why commercial and non‐commercial classification methods vary.

Collaborative ritual classification reveals a shift in the demand for cultural information that may affect the way in which this demand is organized, as well as the classification criteria for works on the digital music market.

Collective creation of a music classification in recommender systems represents a new model of cultural mediation that might change the way of building new uses, tastes and patterns of musical consumption in online environments.

The paper highlights the way in which the classification process might influence the behavior of the users of music information retrieval systems, and vice versa.

Reorganising unstructured academic abstracts according to a certain logical structure can help scholars not only extract valid information quickly but also facilitate the faceted search of academic literature. This study aims to build a high-performance model for identifying of the functional structures of unstructured abstracts in the social sciences.

This study first investigated the structuring of abstracts in academic articles in the field of social sciences, using large-scale statistical analyses. Then, the functional structures of sentences in the abstract in a corpus of more than 3.5 million abstracts were identified from sentence classification and sequence tagging by using several models based on either machine learning or a deep learning approach, and the results were compared.

The results demonstrate that the functional structures of sentences in abstracts in social science manuscripts include the background, purpose, methods, results and conclusions. The experimental results show that the bidirectional encoder representation from transformers exhibited the best performance, the overall F1 score of which was 86.23%.

The data set of annotated social science abstract is generated and corresponding models are trained on the basis of the data set, both of which are available on Github (https://github.com/Academic-Abstract-Knowledge-Mining/SSCI_Abstract_Structures_Identification). Based on the optimised model, a Web application for the identification of the functional structures of abstracts and their faceted search in social sciences was constructed to enable rapid and convenient reading, organisation and fine-grained retrieval of academic abstracts.