Search (31 results, page 2 of 2)

wst: Cut-and-paste-Wissenschaft (2003) 0.01
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Content

"Mikhail Simkin und Vwani Roychowdhury von der University of Califomia, Los Angeles, haben eine in der wissenschaftlichen Gemeinschaft verbreitete Unsitte erstmals quantitativ erfasst. Die Wissenschaftler analysierten die Verbreitung von Druckfehlern in den Literaturlisten wissenschaftlicher Arbeiten (www.arxiv.org/abs/cond-mat/0212043). 78 Prozent aller zitierten Aufsätze - so schätzen die Forscher - haben die zitierenden Wissenschaftler demnach nicht gelesen, sondern nur per 'cut and paste' von einer Vorlage in ihre eigene Literaturliste übernommen. Das könne man beispielsweise abschätzen aus der Analyse fehlerhafter Seitenangaben in der Literaturliste eines 1973 veröffentlichten Aufsatzes über die Struktur zweidimensionaler Kristalle: Dieser Aufsatz ist rund 4300 mal zitiert worden. In 196 Fällen enthalten die Zitate jedoch Fehler in der Jahreszahl, dem Band der Zeitschrift oder der Seitenzahl, die als Indikatoren für cut and paste genommen werden können, denn man kann, obwohl es Milliarden Möglichkeiten gibt, nur 45 verschiedene Arten von Druckfehlern unterscheiden. In erster Näherung ergibt sich eine Obergrenze für die Zahl der `echten Leser' daher aus der Zahl der unterscheidbaren Druckfehler (45) geteilt durch die Gesamtzahl der Publikationen mit Druckfehler (196), das macht etwa 22 Prozent."

Chan, H.C.; Kim, H.-W.; Tan, W.C.: Information systems citation patterns from International Conference on Information Systems articles (2006) 0.01

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Date: 3. 1.2007 17:22:03

H-Index auch im Web of Science (2008) 0.01

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Date: 6. 4.2008 19:04:22

Mingers, J.; Burrell, Q.L.: Modeling citation behavior in Management Science journals (2006) 0.01

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Date: 26.12.2007 19:22:05

Ma, N.; Guan, J.; Zhao, Y.: Bringing PageRank to the citation analysis (2008) 0.01

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Date: 31. 7.2008 14:22:05

Knothe, G.: Comparative citation analysis of duplicate or highly related publications (2006) 0.01

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Gabel, J.: Improving information retrieval of subjects through citation-analysis : a study (2006) 0.01

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Source: Knowledge organization for a global learning society: Proceedings of the 9th International ISKO Conference, 4-7 July 2006, Vienna, Austria. Hrsg.: G. Budin, C. Swertz u. K. Mitgutsch

Safder, I.; Ali, M.; Aljohani, N.R.; Nawaz, R.; Hassan, S.-U.: Neural machine translation for in-text citation classification (2023) 0.01
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Abstract

The quality of scientific publications can be measured by quantitative indices such as the h-index, Source Normalized Impact per Paper, or g-index. However, these measures lack to explain the function or reasons for citations and the context of citations from citing publication to cited publication. We argue that citation context may be considered while calculating the impact of research work. However, mining citation context from unstructured full-text publications is a challenging task. In this paper, we compiled a data set comprising 9,518 citations context. We developed a deep learning-based architecture for citation context classification. Unlike feature-based state-of-the-art models, our proposed focal-loss and class-weight-aware BiLSTM model with pretrained GloVe embedding vectors use citation context as input to outperform them in multiclass citation context classification tasks. Our model improves on the baseline state-of-the-art by achieving an F1 score of 0.80 with an accuracy of 0.81 for citation context classification. Moreover, we delve into the effects of using different word embeddings on the performance of the classification model and draw a comparison between fastText, GloVe, and spaCy pretrained word embeddings.

Hayer, L.: Lazarsfeld zitiert : eine bibliometrische Analyse (2008) 0.01

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Date: 22. 6.2008 12:54:12

Wildner, B.: Web of Science - Scopus : Auf der Suche nach Zitierungen (2006) 0.01

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Date: 4. 6.2006 17:22:15

Ahlgren, P.; Jarneving, B.; Rousseau, R.: Requirements for a cocitation similarity measure, with special reference to Pearson's correlation coefficient (2003) 0.01

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Date: 9. 7.2006 10:22:35

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