Leydesdorff, L.: Alternatives to the Journal Impact Factor: I3 and the Top-10% (or Top-25%?) of the Most-Highly Cited Papers. In: Digital Libraries 1201.4638 (2012),
[Volltext]
Journal Impact Factors (IFs) can be considered historically as the first attempt to normalize citation distributions by using averages over two years. However, it has been recognized that citation distributions vary among fields of science and that one needs to normalize for this. Furthermore, the mean-or any central-tendency statistics-is not a good representation of the citation distribution because these distributions are skewed. Important steps have been taken to solve these two problems during the last few years. First, one can normalize at the article level using the citing audience as the reference set. Second, one can use non-parametric statistics for testing the significance of differences among ratings. A proportion of most-highly cited papers (the top-10% or top-quartile) on the basis of fractional counting of the citations may provide an alternative to the current IF. This indicator is intuitively simple, allows for statistical testing, and accords with the state of the art.
@article{leydesdorff2012alternatives,
author = {Leydesdorff, Loet},
title = {Alternatives to the Journal Impact Factor: I3 and the Top-10% (or Top-25%?) of the Most-Highly Cited Papers},
journal = {Digital Libraries},
year = {2012},
volume = {1201.4638},
url = {http://arxiv.org/abs/1201.4638},
keywords = {scientometrics, factor, analysis, citation, impact, toread},
abstract = {Journal Impact Factors (IFs) can be considered historically as the first attempt to normalize citation distributions by using averages over two years. However, it has been recognized that citation distributions vary among fields of science and that one needs to normalize for this. Furthermore, the mean-or any central-tendency statistics-is not a good representation of the citation distribution because these distributions are skewed. Important steps have been taken to solve these two problems during the last few years. First, one can normalize at the article level using the citing audience as the reference set. Second, one can use non-parametric statistics for testing the significance of differences among ratings. A proportion of most-highly cited papers (the top-10% or top-quartile) on the basis of fractional counting of the citations may provide an alternative to the current IF. This indicator is intuitively simple, allows for statistical testing, and accords with the state of the art. }
}
Leydesdorff, L. & Welbers, K.: The semantic mapping of words and co-words in contexts. , 2010
[Volltext]
Meaning can be generated when information is related at a systemic level.Such a system can be an observer, but also a discourse, for example,operationalized as a set of documents. The measurement of semantics assimilarity in patterns (correlations) and latent variables (factor analysis)has been enhanced by computer techniques and the use of statistics; forexample, in "Latent Semantic Analysis". This communication provides anintroduction, an example, pointers to relevant software, and summarizes thechoices that can be made by the analyst. Visualization ("semantic mapping") isthus made more accessible.
@misc{leydesdorff2010semantic,
author = {Leydesdorff, Loet and Welbers, Kasper},
title = {The semantic mapping of words and co-words in contexts},
year = {2010},
note = {cite arxiv:1011.5209},
url = {http://arxiv.org/abs/1011.5209},
keywords = {ol_web2.0, words, widely_related, semantics, context},
abstract = { Meaning can be generated when information is related at a systemic level.Such a system can be an observer, but also a discourse, for example,operationalized as a set of documents. The measurement of semantics assimilarity in patterns (correlations) and latent variables (factor analysis)has been enhanced by computer techniques and the use of statistics; forexample, in "Latent Semantic Analysis". This communication provides anintroduction, an example, pointers to relevant software, and summarizes thechoices that can be made by the analyst. Visualization ("semantic mapping") isthus made more accessible.}
}