Search (8 results, page 1 of 1)

0.0023790773 = product of:
  0.0047581545 = sum of:
    0.0047581545 = product of:
      0.009516309 = sum of:
        0.009516309 = weight(_text_:a in 477) [ClassicSimilarity], result of:
          0.009516309 = score(doc=477,freq=10.0), product of:
            0.04772363 = queryWeight, product of:
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.041389145 = queryNorm
            0.19940455 = fieldWeight in 477, product of:
              3.1622777 = tf(freq=10.0), with freq of:
                10.0 = termFreq=10.0
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.0546875 = fieldNorm(doc=477)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Abstract

Jorge Hirsch recently proposed the h index to quantify the research output of individual scientists. The new index has attracted a lot of attention in the scientific community. The claim that the h index in a single number provides a good representation of the scientific lifetime achievement of a scientist as well as the (supposed) simple calculation of the h index using common literature databases lead to the danger of improper use of the index. We describe the advantages and disadvantages of the h index and summarize the studies on the convergent validity of this index. We also introduce corrections and complements as well as single-number alternatives to the h index.

Type

a

0.002279905 = product of:
  0.00455981 = sum of:
    0.00455981 = product of:
      0.00911962 = sum of:
        0.00911962 = weight(_text_:a in 2954) [ClassicSimilarity], result of:
          0.00911962 = score(doc=2954,freq=18.0), product of:
            0.04772363 = queryWeight, product of:
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.041389145 = queryNorm
            0.19109234 = fieldWeight in 2954, product of:
              4.2426405 = tf(freq=18.0), with freq of:
                18.0 = termFreq=18.0
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.0390625 = fieldNorm(doc=2954)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Abstract

In a recently published PNAS paper, Radicchi, Fortunato, and Castellano (2008) propose the relative indicator cf as an unbiased indicator for citation performance across disciplines (fields, subject areas). To calculate cf, the citation rate for a single paper is divided by the average number of citations for all papers in the discipline in which the single paper has been categorized. cf values are said to lead to a universality of discipline-specific citation distributions. Using a comprehensive dataset of an evaluation study on Angewandte Chemie International Edition (AC-IE), we tested the advantage of using this indicator in practical application at the micro level, as compared with (1) simple citation rates, and (2) z-scores, which have been used in psychological testing for many years for normalization of test scores. To calculate z-scores, the mean number of citations of the papers within a discipline is subtracted from the citation rate of a single paper, and the difference is then divided by the citations' standard deviation for a discipline. Our results indicate that z-scores are better suited than cf values to produce universality of discipline-specific citation distributions.

Type

a

0.0020106873 = product of:
  0.0040213745 = sum of:
    0.0040213745 = product of:
      0.008042749 = sum of:
        0.008042749 = weight(_text_:a in 444) [ClassicSimilarity], result of:
          0.008042749 = score(doc=444,freq=14.0), product of:
            0.04772363 = queryWeight, product of:
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.041389145 = queryNorm
            0.1685276 = fieldWeight in 444, product of:
              3.7416575 = tf(freq=14.0), with freq of:
                14.0 = termFreq=14.0
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.0390625 = fieldNorm(doc=444)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Abstract

Scientists may seek to report a single definable body of research in more than one publication, that is, in repeated reports of the same work or in fractional reports, in order to disseminate their research as widely as possible in the scientific community. Up to now, however, it has not been examined whether this strategy of "multiple publication" in fact leads to greater reception of the research. In the present study, we investigate the influence of number of articles reporting the results of a single study on reception in the scientific community (total citation counts of an article on a single study). Our data set consists of 96 applicants for a research fellowship from the Boehringer Ingelheim Fonds (BIF), an international foundation for the promotion of basic research in biomedicine. The applicants reported to us all articles that they had published within the framework of their doctoral research projects. On this single project, the applicants had published from 1 to 16 articles (M = 4; Mdn = 3). The results of a regression model with an interaction term show that the practice of multiple publication of research study results does in fact lead to greater reception of the research (higher total citation counts) in the scientific community. However, reception is dependent upon length of article: the longer the article, the more total citation counts increase with the number of articles. Thus, it pays for scientists to practice multiple publication of study results in the form of sizable reports.

Type

a

0.0018615347 = product of:
  0.0037230693 = sum of:
    0.0037230693 = product of:
      0.0074461387 = sum of:
        0.0074461387 = weight(_text_:a in 1608) [ClassicSimilarity], result of:
          0.0074461387 = score(doc=1608,freq=12.0), product of:
            0.04772363 = queryWeight, product of:
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.041389145 = queryNorm
            0.15602624 = fieldWeight in 1608, product of:
              3.4641016 = tf(freq=12.0), with freq of:
                12.0 = termFreq=12.0
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.0390625 = fieldNorm(doc=1608)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Abstract

In this study, we examined empirical results on the h index and its most important variants in order to determine whether the variants developed are associated with an incremental contribution for evaluation purposes. The results of a factor analysis using bibliographic data on postdoctoral researchers in biomedicine indicate that regarding the h index and its variants, we are dealing with two types of indices that load on one factor each. One type describes the most productive core of a scientist's output and gives the number of papers in that core. The other type of indices describes the impact of the papers in the core. Because an index for evaluative purposes is a useful yardstick for comparison among scientists if the index corresponds strongly with peer assessments, we calculated a logistic regression analysis with the two factors resulting from the factor analysis as independent variables and peer assessment of the postdoctoral researchers as the dependent variable. The results of the regression analysis show that peer assessments can be predicted better using the factor impact of the productive core than using the factor quantity of the productive core.

Type

a

0.001719612 = product of:
  0.003439224 = sum of:
    0.003439224 = product of:
      0.006878448 = sum of:
        0.006878448 = weight(_text_:a in 2381) [ClassicSimilarity], result of:
          0.006878448 = score(doc=2381,freq=16.0), product of:
            0.04772363 = queryWeight, product of:
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.041389145 = queryNorm
            0.14413087 = fieldWeight in 2381, product of:
              4.0 = tf(freq=16.0), with freq of:
                16.0 = termFreq=16.0
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.03125 = fieldNorm(doc=2381)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Abstract

All journals that use peer review have to deal with the following question: Does the peer review system fulfill its declared objective to select the best scientific work? We investigated the journal peer-review process at Angewandte Chemie International Edition (AC-IE), one of the prime chemistry journals worldwide, and conducted a citation analysis for Communications that were accepted by the journal (n = 878) or rejected but published elsewhere (n = 959). The results of negative binomial-regression models show that holding all other model variables constant, being accepted by AC-IE increases the expected number of citations by up to 50%. A comparison of average citation counts (with 95% confidence intervals) of accepted and rejected (but published elsewhere) Communications with international scientific reference standards was undertaken. As reference standards, (a) mean citation counts for the journal set provided by Thomson Reuters corresponding to the field chemistry and (b) specific reference standards that refer to the subject areas of Chemical Abstracts were used. When compared to reference standards, the mean impact on chemical research is for the most part far above average not only for accepted Communications but also for rejected (but published elsewhere) Communications. However, average and below-average scientific impact is to be expected significantly less frequently for accepted Communications than for rejected Communications. All in all, the results of this study confirm that peer review at AC-IE is able to select the best scientific work with the highest impact on chemical research.

Content

Vgl. auch: Erratum Re: Selecting manuscripts for a high-impact journal through peer review: A citation analysis of communications that were accepted by Agewandte Chemie International Edition, or rejected but published elsewhere. In: Journal of the American Society for Information Science and Technology 59(2008) no.12, S.2037-2038.

Type

a

0.0016993409 = product of:
  0.0033986818 = sum of:
    0.0033986818 = product of:
      0.0067973635 = sum of:
        0.0067973635 = weight(_text_:a in 4132) [ClassicSimilarity], result of:
          0.0067973635 = score(doc=4132,freq=10.0), product of:
            0.04772363 = queryWeight, product of:
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.041389145 = queryNorm
            0.14243183 = fieldWeight in 4132, product of:
              3.1622777 = tf(freq=10.0), with freq of:
                10.0 = termFreq=10.0
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.0390625 = fieldNorm(doc=4132)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Abstract

In a comprehensive research project, we investigated the predictive validity of selection decisions and reviewers' ratings at the open access journal Atmospheric Chemistry and Physics (ACP). ACP is a high-impact journal publishing papers on the Earth's atmosphere and the underlying chemical and physical processes. Scientific journals have to deal with the following question concerning the predictive validity: Are in fact the "best" scientific works selected from the manuscripts submitted? In this study we examined whether selecting the "best" manuscripts means selecting papers that after publication show top citation performance as compared to other papers in this research area. First, we appraised the citation impact of later published manuscripts based on the percentile citedness rank classes of the population distribution (scaling in a specific subfield). Second, we analyzed the association between the decisions (n = 677 accepted or rejected, but published elsewhere manuscripts) or ratings (reviewers' ratings for n = 315 manuscripts), respectively, and the citation impact classes of the manuscripts. The results confirm the predictive validity of the ACP peer review system.

Type

a

0.0015199365 = product of:
  0.003039873 = sum of:
    0.003039873 = product of:
      0.006079746 = sum of:
        0.006079746 = weight(_text_:a in 2270) [ClassicSimilarity], result of:
          0.006079746 = score(doc=2270,freq=8.0), product of:
            0.04772363 = queryWeight, product of:
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.041389145 = queryNorm
            0.12739488 = fieldWeight in 2270, product of:
              2.828427 = tf(freq=8.0), with freq of:
                8.0 = termFreq=8.0
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.0390625 = fieldNorm(doc=2270)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Abstract

It is essential for research funding organizations to ensure both the validity and fairness of the grant approval procedure. The ex-ante peer evaluation (EXANTE) of N?=?8,496 grant applications submitted to the Austrian Science Fund from 1999 to 2009 was statistically analyzed. For 1,689 funded research projects an ex-post peer evaluation (EXPOST) was also available; for the rest of the grant applications a multilevel missing data imputation approach was used to consider verification bias for the first time in peer-review research. Without imputation, the predictive validity of EXANTE was low (r?=?.26) but underestimated due to verification bias, and with imputation it was r?=?.49. That is, the decision-making procedure is capable of selecting the best research proposals for funding. In the EXANTE there were several potential biases (e.g., gender). With respect to the EXPOST there was only one real bias (discipline-specific and year-specific differential prediction). The novelty of this contribution is, first, the combining of theoretical concepts of validity and fairness with a missing data imputation approach to correct for verification bias and, second, multilevel modeling to test peer review-based funding decisions for both validity and fairness in terms of potential and real biases.

Type

a

0.0010747575 = product of:
  0.002149515 = sum of:
    0.002149515 = product of:
      0.00429903 = sum of:
        0.00429903 = weight(_text_:a in 1007) [ClassicSimilarity], result of:
          0.00429903 = score(doc=1007,freq=4.0), product of:
            0.04772363 = queryWeight, product of:
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.041389145 = queryNorm
            0.090081796 = fieldWeight in 1007, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.0390625 = fieldNorm(doc=1007)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Abstract

Since the 1990s, with the heightened competition and the strong growth of the international higher education market, an increasing number of rankings have been created that measure the scientific performance of an institution based on data. The Leiden Ranking 2011/2012 (LR) was published early in 2012. Starting from Goldstein and Spiegelhalter's (1996) recommendations for conducting quantitative comparisons among institutions, in this study we undertook a reformulation of the LR by means of multilevel regression models. First, with our models we replicated the ranking results; second, the reanalysis of the LR data showed that only 5% of the PPtop10% total variation is attributable to differences between universities. Beyond that, about 80% of the variation between universities can be explained by differences among countries. If covariates are included in the model the differences among most of the universities become meaningless. Our findings have implications for conducting university rankings in general and for the LR in particular. For example, with Goldstein-adjusted confidence intervals, it is possible to interpret the significance of differences among universities meaningfully: Rank differences among universities should be interpreted as meaningful only if their confidence intervals do not overlap.

Type

a