What Makes a Great Journal Great in the Sciences? Which Came First, the Chicken or the Egg?
The paper is concerned with analysing what makes a great journal great in the sciences, based on quantifiable Research Assessment Measures (RAM). Alternative RAM are discussed, with an emphasis on the Thomson Reuters ISI Web of Science database (hereafter ISI). Various ISI RAM that are calculated annually or updated daily are defined and analysed, including the classic 2-year impact factor (2YIF), 5-year impact factor (5YIF), Immediacy (or zero-year impact factor (0YIF)), Eigenfactor, Article Influence, C3PO (Citation Performance Per Paper Online), h-index, Zinfluence, PI-BETA (Papers Ignored - By Even The Authors), Impact Factor Inflation (IFI), and three new RAM, namely Historical Self-citation Threshold Approval Rating (H-STAR), 2 Year Self-citation Threshold Approval Rating (2Y-STAR), and Cited Article Influence (CAI). The RAM data are analysed for the 6 most highly cited journals in 20 highly-varied and well-known ISI categories in the sciences, where the journals are chosen on the basis of 2YIF. The application to these 20 ISI categories could be used as a template for other ISI categories in the sciences and social sciences, and as a benchmark for newer journals in a range of ISI disciplines. In addition to evaluating the 6 most highly cited journals in each of 20 ISI categories, the paper also highlights the similarities and differences in alternative RAM, finds that several RAM capture similar performance characteristics for the most highly cited scientific journals, determines that PI-BETA is not highly correlated with the other RAM, and hence conveys additional information regarding research performance. In order to provide a meta analysis summary of the RAM, which are predominantly ratios, harmonic mean rankings are presented of the 13 RAM for the 6 most highly cited journals in each of the 20 ISI categories. It is shown that emphasizing THE impact factor, specifically the 2-year impact factor, of a journal to the exclusion of other informative RAM can lead to a distorted evaluation of journal performance and influence on different disciplines, especially in view of inflated journal self citations.
|Date of creation:||22 Dec 2010|
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- Chia-Lin Chang & Michael McAleer & Les Oxley, 2011.
"Great Expectatrics: Great Papers, Great Journals, Great Econometrics,"
Taylor & Francis Journals, vol. 30(6), pages 583-619.
- Chia-Lin Chang & Michael McAleer & Les Oxley, 2010. "Great Expectatrics: Great Papers, Great Journals, Great Econometrics," Working Papers in Economics 10/36, University of Canterbury, Department of Economics and Finance.
- Chia-Lin Chang & Michael McAleer & Les Oxley, 2011. "Great Expectatrics: Great Papers, Great Journals, Great Econometrics," Documentos de Trabajo del ICAE 2011-14, Universidad Complutense de Madrid, Facultad de Ciencias Económicas y Empresariales, Instituto Complutense de Análisis Económico.
- Chia-Lin Chang & Michael McAleer & Les Oxley, 2010. "Great Expectatrics: Great Papers, Great Journals, Great Econometrics," KIER Working Papers 714, Kyoto University, Institute of Economic Research.
- Bornmann, Lutz & Daniel, Hans-Dieter, 2009. "Extent of type I and type II errors in editorial decisions: A case study on Angewandte Chemie International Edition," Journal of Informetrics, Elsevier, vol. 3(4), pages 348-352.
- repec:spr:scient:v:85:y:2010:i:1:d:10.1007_s11192-010-0262-0 is not listed on IDEAS
- Schubert, András & Glänzel, Wolfgang, 2007. "A systematic analysis of Hirsch-type indices for journals," Journal of Informetrics, Elsevier, vol. 1(3), pages 179-184. Full references (including those not matched with items on IDEAS)
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