The lost academic home: institutional affiliation links in Google Scholar Citations

Orduña-Malea, Enrique; Ayllon, Juan Manuel; Martín-Martín, Alberto; López-Cózar, Emilio Delgado

doi:10.1108/oir-10-2016-0302

Cited by 11 publications

(3 citation statements)

References 36 publications

(48 reference statements)

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“…With the exception of Péter Jacsó's work, we can only point to two articles written with the goal of directly ascertaining how errors in Google Scholar function and what their impact is: Doğan et al (2016) and Orduna-Malea et al (2017). Other works of great interest, such as those by Harzing and Van der Wal (2008), Baneyx (2008), Li et al (2010), Adriaanse and Rensleigh (2011;2013), and De This means that, in general terms, scholarly literature about errors in Google Scholar, particularly articles focusing on the use of this tool in bibliometric analysis, is scarce, excessively fragmented and diffuse.…”

Section: Discussionmentioning

confidence: 99%

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

Orduña-Malea

Martín-Martín

López-Cózar

2017

Rev. esp. doc. cient.

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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.Keywords: Google Scholar; Academic search engines; Bibliographic databases; Errors; Quality.Google Scholar como una fuente de evaluación científica: una revisión bibliográfica sobre errores de la base de datos Resumen: 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. octubre-diciembre 2017octubre-diciembre , e185 ISSN-L:0210-0614. doi: http://dx.doi.org/10....

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Section: Discussionmentioning

confidence: 99%

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

Orduña-Malea

Martín-Martín

López-Cózar

2017

Rev. esp. doc. cient.

Self Cite

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“…GSP has been extensively analyzed by the literature since its inception. Some studies have focused on its features, such as author metadata (Ortega, 2015a;Orduña-Malea et al, 2017), bibliometric indicators (Teixeira da Silva, 20182021), citation data accuracy (Doğan, Şencan & Tonta, 2016) or even citation manipulation effects (Delgado López-Cózar, Robinson-García & Torres-Salinas, 2014). Other studies have analyzed the coverage of GSP, either global (Ortega & Aguillo, 2014;Ortega, 2015b) or centered on the presence of specific disciplines (Kim & Grofman, 2020), institutions (Mikki et al, 2015;Ortega, 2015c) or research groups (Thoma & Chan, 2019).…”

Section: A Complete Description Of Gsp Is Available In Delgadomentioning

confidence: 99%

Use of Research Organizations Registry (ROR) identifiers in author academic profiles: the case of Google Scholar Profiles

Orduña-Malea

Bautista‐Puig²

2022

Hipertext.net

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Research organizations' persistent identifiers allow for reducing affiliation ambiguities, enable accurate institutional analyses and favor the design of modern online scholarly databases suited for research discovery and research evaluation. However, few studies have attempted to quantify their degree of use. Precisely, the purpose of this work is to determine the use of Research Organizations Registry (ROR) IDs in author academic profiles, specifically in Google Scholar Profiles (GSP). To do this, all the Google Scholar profiles including the term ROR in any of the public descriptive fields were collected and analyzed. The results evidence a low use of ROR IDs (1,033 profiles), mainly from a few institutions (e.g. Pontificia Universidad Javeriana in Colombia, and Escuela Superior Politécnica del Litoral in Ecuador hold 55.7% of all profiles), from low citation-based impact authors (45.1% of profiles attain less than 100 citations each), belonging mainly to Social Sciences (26.3%), Engineering fields (25.3%), and Natural Sciences (22.2%). Although Google Scholar does not facilitate the inclusion of identifiers, it seems that the world's leading research institutions are not recommending their researchers include these identifiers in their profiles yet.

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“…The system sCooL is based on mappings collected from different curated and non-curated sources [9], for which knowledge from CareerBuilder (CB) and Wikipedia and manual mappings are critical. Google Scholar Citations (GSC) provides an institutional affiliation link by institution name and institutional e-mail web domain [16].…”

Section: Related Workmentioning

confidence: 99%

ELAD: An Entity Linking Based Affiliation Disambiguation Framework

et al. 2020

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The number of papers has exploded as more and more people and more types of institutions participating in scientific research. At the same time, institution name disambiguation (IND) is getting more sophisticated, which is critical for research assessment, scholar alignment, etc. Previous knowledge-based and rule-based methods require knowledge and rules prepared in advance, which cannot cope with growing and changing data and learning rules, especially for data with a long period and abundant sources. This paper proposes an automatic learning framework to solve the problem, which is based on entity linking, entity type recognition, candidate generation, and result selection. Experiments show that precision and recall is much higher than the traditional method, ELAD learns more knowledge from the knowledge graph, and it can deal with ever-changing and ever-increasing data. What's more, it solves many problems that cannot be solved by traditional methods: the connection between institution entities, mistakes correction, and the reduction of manual and pre-prepared knowledge. At last, for the case study, we develop two applications based on ELAD which proves its reliability. INDEX TERMS Institution name disambiguation (IND), knowledge graph, entity linking.

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The lost academic home: institutional affiliation links in Google Scholar Citations

Cited by 11 publications

References 36 publications

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

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

Use of Research Organizations Registry (ROR) identifiers in author academic profiles: the case of Google Scholar Profiles

ELAD: An Entity Linking Based Affiliation Disambiguation Framework

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