Google Scholar como una fuente de evaluación científica: una revisión bibliográfica sobre errores de la base de datos

[ES] Google Scholar es un motor de búsqueda académico y herramienta de descubrimiento lanzada por Google (ahora Alphabet) en noviembre de 2004. El hecho de que para cada registro bibliográfico se proporcione información acerca del número de citas recibidas por dicho registro desde el resto de registros indizados en el sistema (independientemente de su fuente) ha propiciado su utilización en análisis bibliométricos y en procesos de evaluación de la actividad académica, especialmente en Ciencias Sociales y Humanidades. No obstante, la existencia de errores, en ocasiones de gran magnitud, ha provocado su rechazo y crítica por una parte de la comunidad científica. Este trabajo pretende precisamente realizar una revisión bibliográfica exhaustiva de todos los estudios que de forma monográfica o colateral proporcionan alguna evidencia empírica sobre cuáles son los errores cometidos por Google Scholar (y productos derivados, como Google Scholar Metrics y Google Scholar Citations). Los resultados indican que el corpus bibliográfico dedicado a los errores en Google Scholar es todavía escaso (n= 49), excesivamente fragmentado, disperso, con resultados obtenidos sin metodologías sistemáticas y en unidades no comparables entre sí, por lo que su cuantificación y su efecto real no pueden ser caracterizados con precisión. Ciertas limitaciones del propio buscador (tiempo requerido de limpieza de datos, límite de citas por registro y resultados por consulta) podrían ser la causa de esta ausencia de trabajos empíricos.[EN] Google Scholar (GS) is an academic search engine and discovery tool launched by Google (now Alphabet) in November 2004. The fact that GS provides the number of citations received by each article from all other indexed articles (regardless of their source) has led to its use in bibliometric analysis and academic assessment tasks, especially in social sciences and humanities. However, the existence of errors, sometimes of great magnitude, has provoked criticism from the academic community. The aim of this article is to carry out an exhaustive bibliographical review of all studies that provide either specific or incidental empirical evidence of the errors found in Google Scholar. The results indicate that the bibliographic corpus dedicated to errors in Google Scholar is still very limited (n=49), excessively fragmented, and diffuse; the findings have not been based on any systematic methodology or on units that are comparable to each other, so they cannot be quantified, or their impact analysed, with any precision. Certain limitations of the search engine itself (time required for data cleaning, limit on citations per search result and hits per query) may be the cause of this absence of empirical studies.Alberto Martin-Martin is on a four-year doctoral fellowship (FPU2013/05863) granted by the Ministerio de Educacion, Cultura y Deportes (Spain). Enrique Orduna-Malea holds a postdoctoral fellowship (PAID-10-14), from the Polytechnic University of Valencia (Spain).Orduña Malea, E.; Martín-Martín, A.; Delgado-López-Cózar, E. (2017). Google Scholar as a source for scholarly evaluation: a bibliographic review of database errors. Revista española de Documentación Científica. 40(4):1-33. https://doi.org/10.3989/redc.2017.4.1500S133404White, B. (2006). Examining the claims of Google Scholar as a serious information source. New Zealand Library & Information Management Journal, 50 (1), 11-24.Wleklinski, J.M. (2005). Studying Google Scholar: wall to wall coverage?. Online, 29 (3), 22-26.Yang, K., & Meho, L. I. (2007). Citation Analysis: A Comparison of Google Scholar, Scopus, and Web of Science. Proceedings of the American Society for Information Science and Technology, 43(1), 1-15. doi:10.1002/meet.1450430118

Why discrepancies in searching the conservation biology literature matter

Conservation biologists seek as much information as possible for evidence-based conservation actions, so they have a special concern for variations in literature retrieval. We assessed the significance for biological conservation of differences in literature retrieval across databases by comparing five simple subject searches in Scopus, Web of Science (WoS) (comparing two different subscriptions), Web of Science (Core Collection) (WosCC) (comparing two different subscriptions) and Google Scholar (GS). The efficiency of a search (the number of references retrieved by a database as a percentage of the total number retrieved across all databases) ranged from 5% to 92%. Different subscriptions to WoS and WoSCC returned different numbers of references. Additionally, we asked 114 conservation biologists which databases they used, their awareness of differing search options within databases and their awareness of different subscription options. The four most widely used databases were GS (88%), WoS (59%), WoSCC (58%) and Scopus (27%). Most respondents (≥ 65%) were unsure about specific features in databases, although 66% knew of the service GS Citations, and 76% agreed that GS retrieved grey literature effectively. Respondents' publication history did not influence their responses. Researchers seeking comprehensive literature reviews should consult multiple databases, with online searches using GS important for locating books, book chapters and grey literature. Comparative evaluations of publication outputs of researchers or departments are susceptible to variations in content between databases and different subscriptions of the same database, so researchers should justify the databases used and, if applicable, the subscriptions. Students value convenience over thoroughness in literature searches, so relevant education is needed

Social impact retrieval: measuring author influence on information retrieval

The increased presence of technologies collectively referred to as Web 2.0 mean the entire process of new media production and dissemination has moved away from an authorcentric approach. Casual web users and browsers are increasingly able to play a more active role in the information creation process. This means that the traditional ways in which information sources may be validated and scored must adapt accordingly. In this thesis we propose a new way in which to look at a user's contributions to the network in which they are present, using these interactions to provide a measure of authority and centrality to the user. This measure is then used to attribute an query-independent interest score to each of the contributions the author makes, enabling us to provide other users with relevant information which has been of greatest interest to a community of like-minded users. This is done through the development of two algorithms; AuthorRank and MessageRank. We present two real-world user experiments which focussed around multimedia annotation and browsing systems that we built; these systems were novel in themselves, bringing together video and text browsing, as well as free-text annotation. Using these systems as examples of real-world applications for our approaches, we then look at a larger-scale experiment based on the author and citation networks of a ten year period of the ACM SIGIR conference on information retrieval between 1997-2007. We use the citation context of SIGIR publications as a proxy for annotations, constructing large social networks between authors. Against these networks we show the effectiveness of incorporating user generated content, or annotations, to improve information retrieval

Defining discovery:is Google Scholar a discovery platform? An essay on the need for a new approach to scholarly discovery

This essay discusses the concept of discovery, intended as content discovery, and defines it in the new context of Open Science, with a focus on Social Sciences and Humanities (SSH). Starting from the example of Google Scholar, the authors show that this well established service does not address the current needs, practices, and variety of discovery. Alternatives in terms of technical choices, features, and governance, do however exist, offering richer and more open discovery. The paper presents in particular the implementations and research work of the H2020 project TRIPLE (Transforming Research through Innovative Practices for Linked Interdisciplinary Exploration). Dedicated to the building of a discovery platform for the SSH, the project is meant to address the specificities and evolution of discovery in this field. Prevailing scholarly resource platforms like Google Scholar limit discovery by focussing only on publications, and favouring through their algorithm well-cited papers, English content, and discipline-specific resources. A limitation in the context of cross-disciplinary and collaborative Open Science, such a service more specifically hinders discovery in the SSH. Characterized by a fragmented landscape, a variety of languages, data types, and outputs, research in the SSH requires services that fully exploit discovery potentialities. Moreover, a survey conducted within the TRIPLE project showed that most SSH researchers use Google Scholar as their starting point, and that they recognise the lack of control they have with this system. Beyond the extension of features and content, transparency is the other important criterion for the building of an Open Infrastructure actually serving the research community. In light of this, we present in some detail the GoTriple platform, which exploits today's technological potential and incorporates the best known functionalities in order to unveil more and innovative scholarly outputs and lead to international and interdisciplinary research project collaborations

De la bibliometría al emprendimiento: un estudio de estudios

Bibliometric studies of entrepreneurship as a discipline have contributed fundamentally to the creation of a certain order in an apparently chaotic and contradictory literature, examining how the discipline has developed, giving a comprehensive vision of the structure of the field, observing its social networks, detecting trends, discovering knowledge gaps and helping to plan future research lines. The purpose of this article is to explore this special type of research. In terms of methodology, it uses an adaptation of the Systematic Literature Review, and a content analysis using text-mining software in order to look deeper into objectives, conclusions and limitations. Among the main findings, there is some evidence that indicates that the image presented to date about entrepreneurship has not considered the multidisciplinary nature of the field and could, therefore, be distorted. At the same time, a series of inherent problems have been detected, and it has become evident that there is a need to incorporate the latest advances in bibliometrics and to improve collaboration between experts from both fields in order to solve those mentioned issues and move towards future progress.Los estudios bibliométricos sobre emprendimiento como disciplina académica han contribuido fundamentalmente a crear orden en una literatura aparentemente caótica y contradictoria, examinan su desarrollo y dan una visión integral de la estructura del campo, observan sus redes sociales, detectan tendencias, descubren brechas de conocimiento y ayudan a planificar futuras líneas de investigación. El objetivo de este artículo es explorar este tipo especial de investigación. Desde el punto de vista metodológico se utiliza una adaptación del proceso de revisión sistemática de la literatura y un análisis de contenido a través de software de minería de textos para profundizar en objetivos, conclusiones y limitaciones de este tipo de análisis. Entre los principales hallazgos encontramos evidencias que indican que la imagen ofrecida hasta la fecha sobre el emprendimiento no ha considerado la naturaleza multidisciplinaria del campo y, por tanto, podría estar distorsionada. A su vez, se detectan una serie de problemas inherentes a su desarrollo, se hace evidente la necesidad de incorporar los últimos avances en bibliometría, mejorando la colaboración entre expertos de ambos campos para resolverlos y avanzar hacia el progreso futuro

Past Themes and Tracking Research Trends in Entrepreneurship: A Co-Word, Cites and Usage Count Analysis

This paper examines the evolution of research in Entrepreneurship published in Web of Science, a reference database. A bibliometric content analysis has been carried out as part of this investigation, allowing for a longitudinal study of the main research topics dealt with over time, ranging from classic topics such as its conception to more recent realities that include Social and Sustainable Entrepreneurship. This paper locates research trends by studying the evolution of citations and by incorporating use metrics. The results point to the existence of seven cognitive fronts that have marked the field’s growth and conceptual evolution. Furthermore, evidence is presented that shows how innovation has historically been the thread that links all the core themes. The topics and trends detected contribute specially to advancing the current discussion on entrepreneurship and coordinating future research efforts

Artificial Intelligence in the Service of Entrepreneurial Finance: Knowledge Structure and the Foundational Algorithmic Paradigm

While the application of Artificial Intelligence in Finance has a long tradition, its potential in Entrepreneurship has been intensively explored only recently. In this context, Entrepreneurial Finance is a particularly fertile ground for future Artificial Intelligence proliferation. To support the latter, the study provides a bibliometric review of Artificial Intelligence applications in (1) entrepreneurial finance literature, and (2) corporate finance literature with implications for Entrepreneurship. Rigorous search and screening procedures of the scientific database Web of Science Core Collection resulted in the identification of 1890 relevant journal articles subjected to analysis. The bibliometric analysis gives a rich insight into the knowledge field's conceptual, intellectual, and social structure, indicating nascent and underdeveloped research directions. As far as we were able to identify, this is the first study to map and bibliometrically analyze the academic field concerning the relationship between Artificial Intelligence, Entrepreneurship, and Finance, and the first review that deals with Artificial Intelligence methods in Entrepreneurship. According to the results, Artificial Neural Network, Deep Neural Network and Support Vector Machine are highly represented in almost all identified topic niches. At the same time, applying Topic Modeling, Fuzzy Neural Network and Growing Hierarchical Self-organizing Map is quite rare. As an element of the research, and before final remarks, the article deals as well with a discussion of certain gaps in the relationship between Computer Science and Economics. These gaps do represent problems in the application of Artificial Intelligence in Economic Science. As a way to at least in part remedy this situation, the foundational paradigm and the bespoke demonstration of the Monte Carlo randomized algorithm are presented

Information knowledge and technology for Development in Africa

Information, knowledge, and technology occupy significant space in the information and knowledge society and ongoing debates on development such as sustainable development goals (SDGs) agenda 2030 and the fourth industrial revolution (4IR). Disruptive technologies and cyber-physical systems, obscuring the lines between the physical, digital and biological, escalated by the COVID-19 pandemic, present a ‘new normal’ that profoundly affects the nature and magnitude of responses required to sustain and benefit from the new developments. Africa, known for late adoption of new technologies and innovations, is leapfrogging development stages in several enviable ways. This book, Information knowledge and technology for development in Africa’, written by eminent African scholars, comprises chapters that satisfactorily address information access, artificial intelligence, information ethics, e-learning, library and information science education (LISE) in the 4IR, data literacy and e-scholarship, and knowledge management, which are increasingly essential for information access, services, and LISE in Africa. We expect the book to support research, teaching and learning in African higher education and worldwide for comparative scholarship