Classic papers: déja vu, a step further in the bibliometric exploitation of Google Scholar

E Delgado López-Cózar, A Martín-Martín, E Orduna-Malea (2017)
Classic papers: déja vu, a step further in the bibliometric exploitation of Google Scholar. EC3 Working Papers, 24. Granada 28th of June, 2017

After giving a brief overview of Eugene Garfield’s contributions to the issue of identifying and studying the most cited scientific articles, manifested in the creation of his Citation Classics, the main characteristics and features of Google Scholar’s new service -Classic Papers-, as well as its main strengths and weaknesses, are addressed. This product currently displays the most cited English-language original research articles by fields and published in 2006.

What does Google Scholar’s Classic Papers offer?

The top 10 most cited English-language original research articles published in 2006 in each of 252 subject categories, according to the data available in Google Scholar as of May 2017. The total number of articles displayed in the product is 2515 articles .

In order to make it to this product, articles must meet the following criteria:

˗ They must have been published in 2006

˗ They must be journal articles, articles deposited in repositories, or conference communications.

˗ The documents must describe original research. Review articles, introductory articles, editorials, guides, commentaries, etc. are explicitly excluded.

˗ They must be written in English.

˗ They must be among the top 10 most cited documents in their respective subject category.

˗ They must have received at least 20 citations.

This product, as could not be otherwise, has the identifying traits of most of Google’s products:

- Simple and straightforward: a list of the most cited articles in each discipline, with a simple browsing interface.

- Easy to use and understand: organized by broad scientific areas and inside of them by subject categories. Three clicks are enough to reach the documents or the public Google Scholar Citations profiles of their authors.

- Minimal information: As a whole, the product displays just over 2500 highly cited articles. Each article presents the most basic bibliographic information.

- Little methodological transparency: It is common for Google Scholar not to declare in detail how their products are developed.

Regarding the las point, there are four critical aspects about which we should know more precise information. They are aspects that could compromise the reliability and validity of the product:

The first of them is related to what Google understands as a research article

The second aspect has to do with the subject classification of the articles.

The third aspect has to do with another crucial issue related to the way Google Scholar works: can we be sure they have successfully merged together all the versions indexed in Google Scholar of these documents? 

The fourth aspect has to do with the threshold selected to consider an article a “classic paper”

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https://doi.org/10.13140/RG.2.2.35729.22880/1

A Novel Improvement to Google Scholar Ranking Algorithms Through Broad Topic Search

Kearl, M., Noteboom, C., Tech, D. (2017
A Novel Improvement to Google Scholar Ranking Algorithms Through Broad Topic Search.  
Proceedings of the Twelfth Midwest Association for Information Systems Conference, Springfield, Illinois May 18-19, 2017

Google Scholar uses ranking algorithms to find the most relevant academic research possible. However, its algorithms use an exact keyword match and citation count to sort its results. This paper presents a novel improvement to Google Scholar algorithms by aggregating multiple synonymous searches into one set of results, offsetting the necessity to guess all potential search phrases for a research topic. This design science research method uses a broad topic analysis that examines search queries, finds synonymous phrases, and combines all keyword searches into one set of results based on current Google Scholar citation count algorithms. To support and evaluate this research-in-progress, several users will compare multiple niche search queries against old and new algorithms. The expectation of this design is to introduce modern algorithm techniques to academic search engines, resulting in greater quality, discoverability, and core topic diversity of published research.