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Mathematics and statistics journals publish papers on the theory and application of mathematics, statistics, and probability. Most mathematics journals have a broad scope that encompasses most mathematical fields. These commonly include logic and foundations, algebra and number theory, analysis (including differential equations, functional analysis and operator theory), geometry, topology, combinatorics, probability and statistics, numerical analysis and computation theory, mathematical physics, etc.
Mathematics and Statistics
A space X is almost star countable (weakly star countable) if for each open cover U of X there exists a countable subset F of X such that
Abstract
In this paper we present a compilation of journal impact properties in relation to other bibliometric indicators as found in our earlier studies together with new results. We argue that journal impact, even calculated in a sufficiently advanced way, becomes important in evaluation practices based on bibliometric analysis only at an aggregate level. In the relation between average journal impact and actual citation impact of groups, the influence of research performance is substantial. Top-performance as well as lower performance groups publish in more or less the same range of journal impact values, but top-performance groups are, on average, more successful in the entire range of journal impact. We find that for the high field citation-density groups a larger size implies a lower average journal impact. For groups in the low field citation-density regions however a larger size implies a considerably higher average journal impact. Finally, we found that top-performance groups have relatively less self-citations than the lower performance groups and this fraction is decreasing with journal impact.
Abstract
In this study the issue of the validity of the argument against the applied length of citation windows in Journal Impact Factors calculations is critically re-analyzed. While previous studies argued against the relatively short citation window of 1–2 years, this study shows that the relative short term citation impact measured in the window underlying the Journal Impact Factor is a good predictor of the citation impact of the journals in the next years to come. Possible exceptions to this observation relate to journals with relatively low numbers of publications, and the citation impact related to publications in the year of publication. The study focuses on five Journal Subject Categories from the science and social sciences, on normal articles published in these journals, in the 2 years 2000 and 2004.
Abstract
Journal impact factors (IFs) can be considered historically as the first attempt to normalize citation distributions by using averages over 2 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.
Abstract
It is shown that the age-independent index based on h-type index per decade, called hereafter an α index instead of the a index, suggested by Kosmulski (Journal of Informetrics 3, 341–347, 2009) and Abt (Scientometrics 2012) is related to the square-root of the ratio of citation acceleration a to the Hirsch constant A.
Abstract
Key to accurate bibliometric analyses is the ability to correctly link individuals to their corpus of work, with an optimal balance between precision and recall. We have developed an algorithm that does this disambiguation task with a very high recall and precision. The method addresses the issues of discarded records due to null data fields and their resultant effect on recall, precision and F-measure results. We have implemented a dynamic approach to similarity calculations based on all available data fields. We have also included differences in author contribution and age difference between publications, both of which have meaningful effects on overall similarity measurements, resulting in significantly higher recall and precision of returned records. The results are presented from a test dataset of heterogeneous catalysis publications. Results demonstrate significantly high average F-measure scores and substantial improvements on previous and stand-alone techniques.
Abstract
Performance measures of individual scholars tend to ignore the context. I introduce contextualised metrics: cardinal and ordinal pseudo-Shapley values that measure a scholar's contribution to (perhaps power over) her own school and her market value to other schools should she change job. I illustrate the proposed measures with business scholars and business schools in Ireland. Although conceptually superior, the power indicators imply a ranking of scholars within a school that is identical to the corresponding conventional performance measures. The market value indicators imply an identical ranking within schools and a very similar ranking between schools. The ordinal indices further contextualise performance measures and thus deviate further from the corresponding conventional indicators. As the ordinal measures are discontinuous by construction, a natural classification of scholars emerges. Averaged over schools, the market values offer little extra information over the corresponding production and impact measures. The ordinal power measure indicates the robustness or fragility of an institution's place in the rank order. It is only weakly correlated with the concentration of publications and citations.
Abstract
The first part of the paper deals with the assessment of international databases in relation to the number of historical publications (representation and relevance in comparison with the model database). The second part is focused on providing answer to the question whether historiography is governed by similar bibliometric rules as exact sciences or whether it has its own specific character. Empirical database for this part of the research constituted the database prepared ad hoc: The Citation Index of the History of Polish Media (CIHPM). Among numerous typically historical features the main focus was put on: linguistic localism, specific character of publishing forms, differences in citing of various sources (contributions and syntheses) and specific character of the authorship (the Lorenz Curve and the Lotka's Law). Slightly more attention was devoted to the half-life indicator and its role in a diachronic study of a scientific field; also, a new indicator (HL14), depicting distribution of citations younger then half-life was introduced. Additionally, the comparison and correlation of selected parameters for the body of historical science (citations, HL14, the Hirsch Index, number of publications, volume and other) were also conducted.
Abstract
Citation distributions are so skewed that using the mean or any other central tendency measure is ill-advised. Unlike G. Prathap's scalar measures (Energy, Exergy, and Entropy or EEE), the Integrated Impact Indicator (I3) is based on non-parametric statistics using the (100) percentiles of the distribution. Observed values can be tested against expected ones; impact can be qualified at the article level and then aggregated.
Abstract
The purpose of this study is to examine efficiency and its determinants in a set of higher education institutions (HEIs) from several European countries by means of non-parametric frontier techniques. Our analysis is based on a sample of 259 public HEIs from 7 European countries across the time period of 2001–2005. We conduct a two-stage DEA analysis (Simar and Wilson in J Economet 136:31–64, 2007), first evaluating DEA scores and then regressing them on potential covariates with the use of a bootstrapped truncated regression. Results indicate a considerable variability of efficiency scores within and between countries. Unit size (economies of scale), number and composition of faculties, sources of funding and gender staff composition are found to be among the crucial determinants of these units’ performance. Specifically, we found evidence that a higher share of funds from external sources and a higher number of women among academic staff improve the efficiency of the institution.
