Newer indices measuring scholarly author impact

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Newer indices measuring scholarly impact

1) Age-weighted citation rate (AWCR, AWCRpA) & AW-index

Inspired by Jin’s The AR-index: complementing the h-index, the AWCR is an age-weighted citation rate where # of citations for a paper is divided by how old it is. Jin defines the AR-index as the square root of the sum of all age-weighted citation counts over all papers that contribute to the h-index. In Publish or Perish, papers are summed over as these represent the impact of the total body of work of a scholar. (This allows younger and less-cited papers to contribute to AWCR even though they may not yet contribute to the h-index.)

 

2) Contemporary h-index

Proposed in Generalized h-index for disclosing latent facts in citation networks, this index aims to improve on the h-index by giving more weight to recent articles, thus rewarding academics who maintain a steady level of activity. Age-related weighting is parametrized; the Publish or Perish implementation uses gamma=4 and-lta=1, like the authors did for their experiments. This means that for an article published during the current year, its citations account four times. For an article published 4 years ago, its citations account only one time. For an article published 6 years ago, its citations account 4/6 times, and so on.

 

3) Eigenfactor

Eigenfactor.org is an academic research project at the University of Washington. Developed by West and Bergstrom, the Eigenfactor is a rating of the total importance of a scientific journal. Eigenfactor is reminiscent of Google’s Pagerank algorithm in that journals are rated according to “link love” or the number of incoming citations. Moreover, citations from highly-ranked journals are weighted higher than poorly-ranked. An Eigenfactor score rises with the total impact of a journal. Therefore, journals that generate a higher impact in the field have a larger (or higher) Eigenfactor score.

Eigenfactor is also used in network analysis to develop methods to evaluate the influence of scholarly journals and map academic outputs in various disciplines.

 

4) Egghe’s g-index

In the Theory and practice of the g-index, Egghe aims to improve on the h-index by giving more weight to highly-cited articles. The g-index is an index for quantifying scientific productivity based on publications and calculated based on the distribution of citations received by a given researcher’s publications. So, given a set of articles ranked in decreasing order of the number of citations that they receive, the g-index is the (unique) largest number such that the top g articles received (together) at least g2 citations.

 

5) E-index

The e-index, complementing the h-index for excess citations is the square root of the surplus of citations in the h-core beyond h^2. One of the aims of the e-index is to differentiate between scientists with identical h-indices but different citations. Another advantage of the e-index is that it can reflect the contributions of highly cited papers of an author, as usually ignored by the h-index. Zhang says that the e-index “is a necessary h-index complement, especially for evaluating highly cited scientists or for precisely comparing the scientific output of a group of scientists having an identical h-index.”

 

6) Google’s I10-index

The I10-index indicates the # of papers an author has written that have been cited at least ten times by other scholars. It was introduced by Google in 2011 as part of their work on Google scholar, a search tool that locates academic and related papers.

 

7) Hirsch’s h-index

see also H-b index

In An index to quantify an individual’s scientific research output, Hirsch aims to provide a single-number metric of an academic’s impact, combining quality with quantity.The H-factor is a measure of impact of individual scientists in their respective fields. When one scientist publishes n articles and is cited n times, an H-factor of n results. This rewards publication of many good articles but few poor ones. It is difficult to increase someone’s H-factor by self-citation (a common problem). One or a few lucky “hits” will alone not improve your H-factor. H-factors become reliable once you have a substantial production of research output. It is important to emphasize that a single number cannot describe a scientist and the H-factor is only one measure of the impact of scholars.Since Hirsch introduced the h index in 2005, this measure of academic impact has garnered widespread interest as well as proposals for other indices based on analyses of publication data such as the g index, h (2) index, m quotient, r index, to name a few. Several commonly used databases, such as Elsevier’s SciVerse Scopus, Thomson Reuters’ Web of Science, Google Scholar’s Citations and Microsoft’s Academic Search, provide h-index values for authors.

Automated computation of the h-index:Quadsearch – http://quadsearch.csd.auth.gr/index.php?lan=1&s=2H-View Visualizer – http://hview.limsi.fr/

 

8) Individual h-index

Proposed in Is it possible to compare researchers with different scientific interests?, this index divides the standard h-index by the average number of authors in articles that have contributed to the h-index calculation in order to reduce the effect of co-authorship. See also Rad AE, Brinjikji W, Cloft HJ, Kallmes DF. The h-index in academic radiology. Acad Radiol. 2010 May 14.

 

9) R-Impact

The Reliability-Based Citation Impact Factor seeks to quantify a journal’s effectiveness, and incorporates citation data over the journal’s lifespan instead of more recent performance histories. see Kuo W, Rupe J. R-Impact: reliability-based citation impact factor. IEEE Transactions on Reliability. 2007;56(3):366-367.

 

10) Universal h-index

In the Universality of citation distributions: toward an objective measure of scientific impact, the tagging of authors with disciplines allows a Tenurometer to compute a new universal h-index. The universal h-index allows researchers to compare the impact of authors in different disciplines with different citation patterns.

 

11) ‘w-index’ or Wu Index

In The w-index: a significant improvement of the h-index, Wu’s index is described as similar to the h-index. According to Hirsch’s criteria, a researcher with an h-index of 9 indicates that he or she has published at least 9 papers, each of which has been cited 9 or more times. The ‘w-index’ indicates that a researcher has published w papers, with at least 10w citations each. A researcher with a w-index of 24 means he or she has 24 papers with at least 240 citations each.

Wu says his index is an improvement on the h-index as it “accurately reflects the influence of a scientist’s top papers”. He says it should be called the “10h-index”. The w-index is easy to calculate using the Web of Knowledge, Scopus (Elsevier) or Google scholar and in the same way as the h-index by searching for a researcher’s name and listing all of their papers in order with the highest cited papers cited first.

 

Source: HLWIKI Canada

 

 

See on hlwiki.slais.ubc.ca

Multidimensional Journal Evaluation Analyzing Scientific Periodicals beyond the Impact Factor

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Scientific communication depends primarily on publishing in journals. The most important indicator to determine the influence of a journal is the Impact Factor. Since this factor only measures the average number of citations per article in a certain time window, it can be argued that it does not reflect the actual value of a periodical. This book defines five dimensions, which build a framework for a multidimensional method of journal evaluation. The author is winner of the Eugene Garfield Doctoral Dissertation Scholarship 2011.

 

Haustein, Stefanie

Multidimensional Journal Evaluation

Analyzing Scientific Periodicals beyond the Impact Factor. Gegruyter, 2012. ISBN:978-3-11-025555-3

Contents: http://www.degruyter.com/view/supplement/9783110255553_Contents.pdf

See on www.degruyter.com

Scientific Mobile Applications: mobile apps for science

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Mobile apps for science are expanding in scope and capability very quickly, yet there is no easy way to source information regarding what is available, what the community thinks of these apps (in terms of general reviews) and clustering of these apps into functional groupings. That is the intention of this wiki. It is a community resource for developers and users to share information about the various science apps that are available.

 

See on www.scimobileapps.com

From ‘Ivory Tower Traditionalists’ to ‘Entrepreneurial Scientists’?

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Growing intensity of university-industry ties has generated an intense debate about the changing norms and practices of academic scientific work. This study challenges the protagonists’ views on the emergence of a dominant market ethos in academic science and growing influence of the ‘new school’ entrepreneurial scientists. It argues that academic scientists are active agents seeking to shape the relationships between science and business, and shows continued diversity in their work orientations. Drawing on neo-institutional theory and the notion of ‘boundary work’, the study examines how scientists seek to protect and negotiate their positions, and also make sense of their professional role identities. It identifies four different orientations: the ‘traditional’ and ‘entrepreneurial’, with two hybrid types in between. The hybrids are the dominant category and are particularly adept at exploiting the ambiguities of ‘boundary work’ between academia and industry. The study is based on 36 interviews and a survey sample of 734 academic scientists from five UK research universities.

 

Source: From ‘Ivory Tower Traditionalists’ to ‘Entrepreneurial Scientists’?

Academic Scientists in Fuzzy University—Industry Boundaries

Alice Lam, School of Management, Royal Holloway University of London, Egham, SurreyPublished online before print February 18, 2010, doi: 10.1177/0306312709349963 Social Studies of Science April 2010 vol. 40 no. 2 307-340

See on sss.sagepub.com

Keeping Your Faculty Salaries Competitive; the AACSB Datadirect service delivers the intelligence for benchmarking your school

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Salaries are always important; however, in our current economy with budget cuts and funding changes, it can be difficult to know whether your school’s salaries are staying competitive to help you find the best and most qualified faculty. AACSB International collects information on full-time faculty salaries on an annual basis. Reports showing the average salaries by discipline and faculty rank are released in January each year based on the most recent survey. To really get a better idea of how average salaries may be changing, a controlled set of 480 schools that participated in all years from 2009–10 to 2011–12 was used to consider any ongoing salary fluctuations.

The table above provides some background to the data included. In this set of 480 schools, approximately 28,000 total full-time faculty were reported in each year. As can be seen from the average salaries listed, there has been slow but steady growth in salaries overall.

 

Source: AACSB Datadirect

https://datadirect.aacsb.edu/

See on www.aacsb.edu

The Rise of Digital Influence; Reach, Resonance, and Relevance

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Digital Influence is one of the hottest trends in social media, yet is largely misunderstood. “The Rise of Digital Influence,” the new report by Altimeter Group Principal Analyst Brian Solis, is a ’how-to’ guide for businesses to spark desirable effects and outcomes through social media influence. The report helps companies understand how influence spreads, and includes case studies in which brands partnered with vendors to recruit connected consumers for digital influence campaigns. Brian evaluates the offerings of 14 Influence vendors, organizing them by Reach, Resonance, and Relevance: the Three Pillars that make up the foundation for Digital Influence as defined in the report. Also included are an Influence Framework and an Influence Action Plan to help brands identify connected consumers and to define and measure strategic digital influence initiatives.

 

Source: The Rise of Digital Influence

by Altimeter Group Network on SlideShare on Mar 20, 2012

http://www.slideshare.net/Altimeter/the-rise-of-digital-influence

See on www.slideshare.net

Social Networks for Scientists; more impact through collaboration

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New social networks and media are improving the connectivity of researchers, engineers, PhD candidates, post-docs, and students. Today, several offer solutions to problems faced by researchers, but are still often considered time consuming. As an online extension of the work of your team or as a catalyst for new collaborations, each of these networks has its own special features. Do you need to optimize your literature review, share or obtain information, dialogue with an instructor, or even reinforce your network of contacts? Whether researcher or student, MyScienceWork presents an overview of the new scientific social networks dedicated to your needs.

 

Source: 14 May 2012 | by Laurence Bianchini

See on blog.mysciencework.com

Professors as intellectual leaders: formation, identity and role

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 The literature on leadership in higher education is predominantly concerned with the role of formally designated senior managers such as heads of department and deans of faculty. By contrast, relatively little attention has focused on those performing informal and distributed forms of leadership, such as (full) university professors. This article draws on the results of an online questionnaire and interviews to explore the leadership role of professors, primarily in a UK context. Professors feel that there is a mismatch between their priorities and those of their employing institutions and that their expertise is under‐utilised. A number of qualities are identified which may be associated with the role of a professor as an intellectual leader: role model, mentor, advocate, guardian, acquisitor and ambassador. It is argued that new managerialism and performative expectations are reshaping the role of the professoriate, and that institutions need to do more to develop their leadership capacity.

 

The literature on leadership in higher education is predominantly concerned with the role of formally designated senior managers such as heads of department and deans of faculty. By contrast, relatively little attention has focused on those performing informal and distributed forms of leadership, such as (full) university professors. This article draws on the results of an online questionnaire and interviews to explore the leadership role of professors, primarily in a UK context. Professors feel that there is a mismatch between their priorities and those of their employing institutions and that their expertise is under‐utilised. A number of qualities are identified which may be associated with the role of a professor as an intellectual leader: role model, mentor, advocate, guardian, acquisitor and ambassador. It is argued that new managerialism and performative expectations are reshaping the role of the professoriate, and that institutions need to do more to develop their leadership capacity.

 

Bruce Macfarlanea (2011). Professors as intellectual leaders: formation, identity and role. Studies in Higher Education: Vol. 36, No. 1, pp. 57-73.

DOI:10.1080/03075070903443734

See on www.tandfonline.com

the new Google Knowledge Graph; why universities need semantic smart personal pages: it’s about professor presence and impact

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Search is a lot about discovery—the basic human need to learn and broaden your horizons. But searching still requires a lot of hard work by you, the user. So today I’m really excited to launch the Knowledge Graph, which will help you discover new information quickly and easily.
Take a query like [taj mahal]. For more than four decades, search has essentially been about matching keywords to queries. To a search engine the words [taj mahal] have been just that—two words.

But we all know that [taj mahal] has a much richer meaning. You might think of one of the world’s most beautiful monuments, or a Grammy Award-winning musician, or possibly even a casino in Atlantic City, NJ. Or, depending on when you last ate, the nearest Indian restaurant. It’s why we’ve been working on an intelligent model—in geek-speak, a “graph”—that understands real-world entities and their relationships to one another: things, not strings.

The Knowledge Graph enables you to search for things, people or places that Google knows about—landmarks, celebrities, cities, sports teams, buildings, geographical features, movies, celestial objects, works of art and more—and instantly get information that’s relevant to your query. This is a critical first step towards building the next generation of search, which taps into the collective intelligence of the web and understands the world a bit more like people do.

Google’s Knowledge Graph isn’t just rooted in public sources such as Freebase, Wikipedia and the CIA World Factbook. It’s also augmented at a much larger scale—because we’re focused on comprehensive breadth and depth. It currently contains more than 500 million objects, as well as more than 3.5 billion facts about and relationships between these different objects. And it’s tuned based on what people search for, and what we find out on the web.

See on www.google.com

Google starts ranking journals with service called Google Scholar Metrics

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Google announced a new feature to its Scholar service. This was no prank. It was the genuine debut of a new tool called Google Scholar Metrics. The service follows the same principle that has made Google’s web search engine so successful – when you are unsure what a user is looking for, give them a list of options ranked by a metric of popularity. In this instance, the users are academics ready to submit their next breakthrough but are uncertain which journal to choose. The solution Scholar Metrics offers is a database summarizing the sway of the distributors of scholarship “to help authors as they consider where to publish their new research”.Here’s how it works. Google creates a list of all the articles a journal has published in a specified period of time. The citations to each article are counted in order to determine the publication’s h-index, which is the largest number “h” such that each of the set of “h” articles were cited “h” or more times. As an example of how the h-index is calculated, consider a publication that has had six total articles having 2, 18, 11, 3, 22, and 9 citations, respectively. This gives the journal an h-index of four. Articles meeting the h-index criterion constitute the h-core. In the example, the core is the articles with 18, 11, 22 and 9 citations. Within the h-core, the median of the citation counts is used to assess the typical influence among the most highly cited set and is reported as the h-median. In the example, the h-median is 14.5.

See on www.significancemagazine.org