No open-access location is known for this record yet.
No open-access location is known for this record yet.
Study the past if you would define the future. In 1955, Dr Eugene Garfield published a revolutionary article in Science that forever altered how we measure the impact of scientific articles. In his article, “Citation Indexes for Science: A New Dimension in Documentation Through Association of Ideas,”1 he proposed what would become the Science Citation Index, offering academic journals an Impact Factor (IF) to gauge the importance of their published research. As I sit here on my MacBook Air in 2014 hearing the ping of new emails and the ding of incoming texts while reading Garfield’s article, which describes early tools like Shepard’s Citations developed in 1873,1 I’m struck by how far we’ve come in 60 years. I think it’s fair to say that prior to drafting a new manuscript, most authors will scour the literature, research via PubMed and PubMed Central, and Google search for redundancy to their own proposed work, asking questions such as: Has someone already tackled this problem? Who was it? When was it? How is my work (potentially) better or more novel? What can I add to the academic literature to justify the publication of this work? The days of singularly consulting print volumes are behind us—the “digital generation” has thrust itself upon us all. The importance of the IF as a measurement tool cannot be overstated, and we believe in its validity as a gauge to monitor the performance and success of the Aesthetic Surgery Journal (ASJ). We will continue our efforts to focus on high-impact articles that will improve the IF score. However, for the past several years, the literature has seen the addition of articles refuting its omnipresence and bemoaning its emphasis on Review articles over new research. The debate over the science and relevance of the IF in varying disciplines is likely one that will continue ad nauseum. The loudest voices in the argument may be those whose scores remain modest year over year, or whose content is generally not cited until more than 2 years postpublication, such as in mathematics, leaving them with a slower response or validation of their publication.2 It’s unlikely that Thomson Reuters, which calculates and distributes IF scores each June (but delayed them until July 29 this year so they could launch InCites to offer more metrics and analysis for authors), will modify the Journal Citation Reporting (JCR) to its 225 subjects in Science and Social Science to respond to the disciplines that question its utility. Some authors or editors may choose to place less emphasis on it while others may endeavor to manipulate the system and artificially inflate their own journal’s scores. But the odds are guaranteed that by publishing in a journal with an IF, your work will become a part of the bibliometric data set that fluctuates each year and yields new scores for academic journals. The technological landscape has advanced light years since the advent of the Science Citation Index and IF score reporting. So why is it still the gold standard? Probably because academic institutions base promotions partly on an individual’s publications, linking the weight and gravitas of the papers to the IF of the journal in which they appear. One might argue: we continue using it in the absence of something better. I would argue there is something new in the landscape that offers journals and authors the ability to ascertain the societal impact of an article and, by extension, a journal much more rapidly than relying solely on the IF score. A supplement, if you will. Altmetrics (which stems from “article-level metrics”) may be considered a modern-day complement to the IF score. By calculating every tweet, like, knowledge base hit, blog, and mass media mention, Altmetrics LLC (London, UK) and other competitors can calculate a score or “badge” for every published article. It recently posted its top 100 ranking for articles published in 2013. The top one scored 7405, as illustrated in Figure 1. You can see the overwhelming majority of social interest hails from Twitter followed by Facebook. Altmetrics was formed in 2011, so the technology is relatively new and nimble, because it must be to offer a competitive solution or supplement in this space. Tracking the life of an individual article allows us to discern how it is read and used, which may validate journal publication strategies. Think of it as a quick pulse for (potentially) nonexperts in the field reading an article; they can assess relevance to their own work today versus waiting months or years as citations collect. Figure 2 shows that the second-ranked article garnered a much lower score, but the base of social media contributions was much more diverse. The buzz about Altmetrics has caught on as a supplement to the traditional IF score methodology—and new competitors such as ImpactStory (Carrboro, New Jersey) have joined the party, receiving sizable grants from the National Science Foundation and the Alfred P. Sloan Foundation. It’s important to note that Altmetrics is being applied to articles published in journals with an IF, where the influence of IF is linked to the journal’s importance in the discipline. To test altmetrics on your own article, you can download the bookmarklet here and then “altmetric it” with one click: http://www.altmetric.com/bookmarklet.php. According to Altmetrics, this article ranked first in 2013 with a score of 7405. It was published in Scientific Reports Open Access. It’s important to consider that the open-access medium may have contributed to the higher score. http://www.altmetric.com/top100. Accessed May 21, 2014. The second-ranked article in 2013 was published in New England Journal of Medicine and focused on cardiovascular disease prevention. The contributions to the score of 2260 are more varied than in Figure 1, but Twitter is still the greatest contributing factor, followed next by Facebook. http://www.altmetric.com/top100. Accessed May 21, 2014. We ran the Altmetrics algorithm on a recently published ASJ article by Stevens et al3 that scored a 15; it received 1 news outlet pickup, 6 tweets, 3 likes on Facebook, and 2 readers on Mendeley (Figure 3). Another ASJ article by Swanson4 received a score of 24 based solely on 3 news outlet contributions. Our n=2 test parried the top Altmetrics articles in the sense that some garnered diverse social media contributions and others did not. It will be interesting to observe the trends in this area as more journals and authors take advantage of this alternative scoring system. The Altmetric score of a recently published ASJ article. The score reflects a nice variety of social media components; news outlet coverage carries the strongest “weight” in the algorithm that comprises the score. Score calculated using an Altmetric bookmarklet available at: http://www.altmetric.com. You may be wondering: how can we be sure Altmetrics really works, and what is the relationship (if any) to IF citations? The same question has been asked in publishing about the relationship between article downloads (commonly called usage statistics) and citations—with many erroneously assuming high usage begets high citations. According to Thelwall et al,5 “Statistically significant associations were found between higher metric scores and higher citations for articles with positive Altmetric scores in all cases with sufficient evidence (tweets, Facebook wall posts, research highlights, blog mentions, mainstream media mentions and forum posts) except perhaps for Google+ posts.” Thelwall et al compared 11 altmetric sources ranging from 76 to 208 739 “hits” with Web of Science citations to 208 739 PubMed articles that garnered at least 1 altmetric mention. They use a lowercase a to describe altmetrics as an umbrella term for any and all alternative metrics such as Twitter, Facebook, blog posts, and so on, whereas I have been capitalizing Altmetrics to reflect both the company name and the calculated score itself. Thelwall et al caution us to consider the effect of time when comparing the relationship between traditional citations and altmetrics because articles published at different times, even within the same year, can remove or reverse this association.5 At the ASJ, we engage all facets of social media to expand the reach of the content we publish for the benefit of the authors, the Journal, and the specialty as a whole. We encourage our authors to sign up for professional Twitter and Facebook accounts and to connect with their colleagues through LinkedIn, a professional networking site. Through these avenues, aesthetic surgeons can leverage social media opportunities to their own professional and academic advantage. If ASJ, its authors, and its readers partner together, we can share ideas and research among our respective networks to elevate the community and spotlight aesthetic surgery both nationally and internationally. Since social media and its derivatives move faster than a teenager texting, articles are afforded an early assessment of impact. Finally, it helps authors connect with the academic community over shared interest in a field of expertise, improve discoverability, and, it is suspected, increase article citations. We study history to avoid repeating mistakes but also to guide us in our quest to innovate and push the envelope. Of the IF tool, Garfield1 said, “The new bibliographic tool, like others that already exist, is just a starting point in literature research. It will help in many ways, but one should not expect it to solve all our problems.” There may never be a one-stop shopping resource that answers every question posed in the medical and scientific spheres, but we can leverage altmetrics and social media and embrace the IF’s strong historical performance to ensure that optimal academic and societal impact is afforded to scholarly work now and for future generations of aesthetic plastic surgeons.
Loading…
Stored only when both papers are in the corpus — 32 citations exist upstream.
Papers that cite the same references — the relational fallback while embeddings are still backfilling.
Imported from OpenAlex (CC0) — nothing here is generated.