Kritik an Google Scholar

Aaron Tay: Why EXACTLY is Google Scholar bad for evidence synthesis, systematic reviews?, in: Aaron Tay’s Musings about librarianship 12.05.2024
1. Google Scholar ranks at most 1,000 results.
2. Google Scholar has no official bulk export function
3. Google Scholar lacks features needed for high recall, high precision searching.
4. Google Scholar is not a database?!?! or Google Scholar crawls the web so it’s index is unstable
5. Google Scholar is a black box, the algorithm is not transparent or known
6. We do not understand how Google Scholar works, results are not interpretable or explainable
7. Google Scholar is just not reproducible

Betrug bei Google Scholar

Hazem Ibrahim, Fengyuan Liu, Yasir Zaki1, Talal Rahwan: Google Scholar is manipulatable, Studie auf arxiv.org 07.02.2024 zeigen auf: Fake Artikel können bei Google Scholar gelistet werden und Fake-Zitierungen können bei Firmen bestellt werden. „Citations are widely considered in scientists’ evaluation. As such, scientists may be incentivized to inflate their citation counts. While previous literature has examined self-citations and citation cartels, it remains unclear whether scientists can purchase citations. Here, we compile a dataset of ∼1.6 million profiles on Google Scholar to examine instances of citation fraud on the platform. We survey faculty at highly-ranked universities, and confirm that Google Scholar is widely used when evaluating scientists. Intrigued by a citation-boosting service that we unravelled during our investigation, we contacted the service while undercover as a fictional author, and managed to purchase 50 citations. These findings provide conclusive evidence that citations can be bought in bulk, and highlight the need to look beyond citation counts.“

Lücken in Free-Access Datenbanken

Lorena Delgado-Quiros, Isidro F. Aguillo, Alberto Martín-Martín, Emilio Delgado Lopez-Cozar, Enrique Orduña-Malea, José Luis Ortega: Why are these publications missing? Uncovering the
reasons behind the exclusion of documents in free-access scholarly database, Journal of the Association for Information Science and Technology 2023
untersuchen, warum free Access Akademische Datenbanken wissenschaftliche Publikationen nicht vollständig erfassen: „The results show that coverage differences are mainly caused by the way each service builds their databases. While classic bibliographic databases ingest almost the exact same content from Crossref (Lens and Scilit miss 0.1% and 0.2% of the records, respectively), academic search engines present lower coverage (Google Scholar does not find: 9.8%, Semantic Scholar: 10%, and Microsoft Academic: 12%). Coverage differences are mainly attributed to external factors, such as web accessibility and robot exclusion policies (39.2%–46%), and internal requirements that exclude secondary content (6.5%–11.6%).“