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The article provides theoretical and empirical justification for the use of multivariate methods of analysis to implement different types of models in the study of the productivity of involuntary and combined memorization by students in learning chemistry.
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... It should be noted that when, instead of top 300 recognitions, more detailed output (e.g.Web of Science articles) and, instead of population, more detailed input (research years) measures are available, it is possible to execute more refined and up-to-date measuring of research productivity. However, here we cannot dwell deeper into productivity analyses which we have done elsewhere [see 14,15]. ...
In this paper the reputation of the centuries-old Anglo-American university model is scrutinized utilizing the top 100 lists of global university rankings. The potential newcomers to the top 100 lists are also traced among the top 100 universities under 50 years old. In addition to reputation of universities, getting to the top 300 lists of university research in six fields is examined, with a special emphasis on the division between English and non-English-speaking countries. The effect of the country size in making the top 300 research in various fields is examined as well.
... International experts on quantitative science and technology research are well aware of the challenges and solutions in making comparisons across disciplines (see Moed et al. 2004, also Abramo et al. 2012. Our own solution has for some years now been to develop a field-specific productivity analysis of scientific action (see Hedman 2004, 2008;Kivinen et al. 2011 in which universities from one nation, region, or some EU-type of supranational organisation are brought together into a relational input-output analysis by disciplines. Following the recommendations of the OECD, the European Commission or suchlike, most governments try to carry out evidence-based science policy for the sake of the allegedly bright future of the knowledge economy. ...
... Let us point out, as concerns the data of this study, that a one-year cross-sectional dataset is alone probably a bit shaky for instance to yield a sound enough base for evidence-based policy. In our earlier work, (Kivinen et al. 2011) we have stated that longitudinal data from a five-year period begins to be reliable enough to control variations; Moed (2005). 4 Input data for the Chonbuk National University (Tec) and the University of Ulsan (Med) from South Korea, Malmö University (Med) from Sweden as well as Chang Gung University (Med) and the National Chung Hsing University (Tec) from Taiwan have been complemented from national and local sources due to their unavailability in the QS. 5 To calculate disposable man years DMY from a known total amount of resources (TF) and a known total amount of 'recognized units' (in this case 161 from which 95 in Sci, Tec or Med). ...
The article introduces a relational input–output model for the productivity analysis of university research. The comparative analyses focus on top university research in hard sciences from 4 East Asian countries (Hong Kong, Singapore, South Korea, Taiwan) and 4 North European countries (Denmark, Finland, Norway, Sweden), universities of which get altogether 95 recognitions in the HEEACT Top 300 rankings in the Natural Sciences (Sci), Technology (Tec) or Clinical Medicine (Med). According to productivity ratings (A0, A, A+, A++), Taiwan receives 10 A++ ratings (Sci 5, Tec 5), Sweden 9 (Sci 4, Med 4, Tec 1) and Hong Kong 9 (Tec 4, Med 2, Sci 1). The smallest numbers of A++ ratings are found in Norway, 1 (Med) and Finland 3 (all in Med). The only university with an A++ rating in the top of all three fields is the National University of Singapore. The Pohang University of Science and Technology (South Korea) and the National Tsing Hua University (Taiwan) are exceptionally productive in Sci and Tec; Karolinska Institutet (Sweden) and the University of Helsinki (Finland) belong to the top in Med. Even though Northern European countries are ranked higher in the ‘knowledge economy indicators’, East Asians fare better by indicators of learning outcomes and by productivity of university research in Natural Sciences and Technology; North European countries are stronger in Clinical Medicine.
... International experts on quantitative science and technology research are well aware of the challenges and solutions in making comparisons across disciplines (see Moed et al. 2004, also Abramo et al. 2012. Our own solution has for some years now been to develop a field-specific productivity analysis of scientific action (see Hedman 2004, 2008;Kivinen et al. 2011 in which universities from one nation, region, or some EU-type of supranational organisation are brought together into a relational input-output analysis by disciplines. Following the recommendations of the OECD, the European Commission or suchlike, most governments try to carry out evidence-based science policy for the sake of the allegedly bright future of the knowledge economy. ...
... Let us point out, as concerns the data of this study, that a one-year cross-sectional dataset is alone probably a bit shaky for instance to yield a sound enough base for evidence-based policy. In our earlier work, (Kivinen et al. 2011) we have stated that longitudinal data from a five-year period begins to be reliable enough to control variations; Moed (2005). 4 Input data for the Chonbuk National University (Tec) and the University of Ulsan (Med) from South Korea, Malmö University (Med) from Sweden as well as Chang Gung University (Med) and the National Chung Hsing University (Tec) from Taiwan have been complemented from national and local sources due to their unavailability in the QS. 5 To calculate disposable man years DMY from a known total amount of resources (TF) and a known total amount of 'recognized units' (in this case 161 from which 95 in Sci, Tec or Med). ...
The global university rankings are here to stay. The ranking positions tell the universities’ recognized success in the reputation markets. Whether deliberate or not, the rankings tend to establish a ‘single norm of excellence’ and reduce complex global higher education landscape into an ordinal order from best to worst. (Hazelkorn, Kauppi & Erkkilä; EAU) If governments are to strive for evidence-based science policy, they have to know, how productive the research in a country is, especially in such costly and facility intensive fields like natural sciences, technology and clinical medicine. The more detailed question is, which institutions produce the best or worst results and at what price. The emergence of Asian universities and scientific research is actualising in inreasing numbers of appearances in the global university rankings (ARWU, THES, QS and HEEACT) hitherto dominated by North American universities accompanied by some European universities.
The productivity analysis utilizes input (research man-years) and output (Web of Science articles and articles in Hi-impact journals) data of 48 Top 300 ranked East Asian and North European universities. Based on the results of the analyses the 48 universities are rated from A0 (output falling short of input) to A++ (output exceeding input). The paper focuses on Korea, Taiwan, Singapore and Hong Kong from East Asia vis-à-vis Finland, Norway, Denmark and Sweden from North Europe, all of which belong to PISA elite (Programme for International Student Assessment) in science and mathematics. A high rank in science and mathematics PISA shows high level of learning outcomes which according to our understanding indicates that the “school culture” of the country has a specific “science ethos” and students’ high academic potentiality. Finally, the paper shows how the “science ethos” indicated by PISA manifests in productivity of research in natural sciences, technology or clinical medicine.
