Wilson López-López


The growth in production and diffusion of knowledge worldwide has been joined by a fortunate growth in impact measurement systems (Arencibia Jorge & de Moya Anegón, 2008; Guerrero Bote, Olmeda-Gómez, & de Moya-Anegón, 2013; Sugimoto et al., 2013). Other measures have joined the traditional Impact Factor (IF) from the Journal Citation Reports (JCR), part of the Thomson Reuters Web of Science (WOS), and the Scimago Journal Rank (SJR) based on Elsevier’s Scopus rankings. These new measures supplement scientometric analyses of scientific output in different areas. Amongst these new indicators we find the SNIP, which measures the contextual impact of the journal, the IPP, which measures the impact per published paper, the web-based Eigenfactor (http://eigenfactor.org), which assesses the importance of a journal (Bergstrom, West, & Wiseman, 2008), the h factor or metrics derived from Google Scholar. This trend is on the rise and new indicators aimed to enrich the understanding of scientific publication will probably appear (Acevedo-Triana, López-López, & Parra, 2014; McKerlich, Ives, & McGreal, 2013).

These and other derived indicators like quartiles and deciles and h and g indexes, are tools that not only allow us to understand scientific communication, but that have taken upon a significant role in assessing researchers, journals, training programmes, institutions and even regions and countries. These assessments have an effect on resource flows for research and accreditation systems in institutions (Batista, Campiteli, Kinouchi, & Martinez, 2006; Macilwain, 2010, 2013).

Their importance is clear and it is better to try and understand them in order to find criticisms or to improve upon them than to ignore them. Information and Communication Technologies (ICT), especially the Web, have created new forms of communication and evaluation. Alternative measuring systems have been developed as well from the Web: Webmetrics emerge around 1990 according to Cabezas and Jiménez (2013) and their goal is to measure everything related to the web, from economic and social aspects to technical issues. Therefore, it goes beyond traditional bibliometric methodologies, even though those are used within Webmetrics. In any case, new indicators such as downloads, visits, recommendations and others derived from content management in the Web come to play, showing supplementary patterns (Torres-Salinas, Cabezas-Clavijo, & Jiménez-Contreras, 2013).
The speed with which these indicators can be created and analysed is critical, since real-time information can nearly always be provided, detailed by users, locations and usage times.

Thanks to this, gray literature, that is, buried, never-cited papers, and even informal conversations, can live and have an effect on knowledge communication dynamics. Authors can bring importance to discussions on Facebook, Twitter, blogs, and academic networks such as Mendeley, Academia.edu and ResearchGate) – with effects that we have yet to discover.

Today, we can classify usages of digital libraries (number of readers, group), social network mentions (Facebook, Google+, Twitter), blog posts, encyclopedias (Wikipedia, Scholarpedia), news content aggregators, and when all our training programmes are open we will be able to more clearly understand the use of research resources in training processes. Nowadays, the Internet makes it possible for data to be shared and for knowledge building to be performed by diverse communities in different places in the world, and it can be a more efficient resource for peer-review and plagiarism control.

Evidently, Altmetrics and other resources are under development. Hence, we must think of the interaction possibilities and the ability to supplement existing information that bibliometric and Altmetrics indicators bring about, together with new dynamics for peer review. Nevertheless, and perhaps this is their most relevant contribution, thanks to them we are about to stop depending on private, closed indicators and we can now openly measure the relationship between academic and social appropriation of knowledge, which will enable us to create new ways in which scientific communities and society at large can communicate. Altmetrics is in its infancy but alternative metrics will surely transform communication and scientific knowledge production. We need to think about these new resources and prepare to efficiently assume these new dynamics in order to enrich our static evaluation systems.


Acevedo-Triana, C., López-López, W., & Parra, F. C. (2014). Recomendaciones en el diseño, la ejecución y la publicación de investigaciones en Psicología y ciencias del comportamiento. Revista Costarricense de Psicología, 33(2), 155–177.

Arencibia Jorge, R., & de Moya Anegón, F. (2008). La evaluación de la investigación científica: una aproximación teórica desde la cienciometría. ACIMED, 17(4).

Batista, P. D., Campiteli, M. G., Kinouchi, O., & Martinez, A. S. (2006). Is it possible to compare researchers with different scientific interests? Scientometrics, 68(1), 179–189. doi:10.1007/s11192-006-0090-4

Bergstrom, C. T., West, J. D., & Wiseman, M. A. (2008). The EigenfactorTM Metrics. Journal of Neuroscience, 28(45), 11433–11434. doi:10.1523/JNEUROSCI.0003-08.2008

Guerrero Bote, V. P., Olmeda-Gómez, C., & de Moya-Anegón, F. (2013). Quantifying the benefits of international scientific collaboration. Journal of the American Society for Information Science and Technology, 64(2), 392–404. doi:10.1002/asi.22754

Macilwain, C. (2010). Science economics: What science is really worth. Nature, 465(7299), 682–4. doi:10.1038/465682a

Macilwain, C. (2013). Emerging powers need a more-inclusive science. Nature, 505, 7.

McKerlich, R., Ives, C., & McGreal, R. (2013). Measuring use and creation of open educational resources in higher education. International Review of Research in Open and Distance Learning, 14(4), 90–103. doi:10.1002/asi

Sugimoto, C. R., Thelwall, M., Larivière, V., Tsou, A., Mongeon, P., & Macaluso, B. (2013). Scientists popularizing science: characteristics and impact of TED talk presenters. PloS One, 8(4), e62403. doi:10.1371/journal.pone.0062403

Torres-Salinas, D., Cabezas-Clavijo, Á., & Jiménez-Contreras, E. (2013). Altmetrics: New Indicators for Scientific Communication in Web 2.0. Comunicar, 21(41), 53–60. doi:10.3916/C41-2013-05


Cómo citar
López-López, W. (1). Altmetrics and other Alternative Indicators to Measure Knowledge Diffusion. Universitas Psychologica, 13(5), 1645. Recuperado a partir de https://revistas.javeriana.edu.co/index.php/revPsycho/article/view/12549