The Comparative Study of Ranking System of Islamic Countries Universities and National Ranking of Universities in Iran Using the Most Famous Ranking Systems in the World

This research is aiming at a comparative study of Islamic countries university`s ranking system and Iran`s universities` national ranking system with the most famous ranking system in the world which are as Academic Ranking of World University (ARWU), The Times Higher Education World University Rankings (THE), The Quacquarelli Symonds World University Rankings (QS), The U.S. News rankings (USN), Center for World University Rankings (CWUR). In this research, the criteria and indicators of each of the five higher education ranking systems are described based on the two ranking systems of ISC and National Ranking of universities of Iran and using George Bradley's comparative analysis and considering the list of top universities in the last update of the Internet portal by the date 05/09/2017. In this research, it is revealed that there is not any similarity between QS ranking system and ISC ranking system. However, based on the results, Iran and ISC ranking systems are mostly compatible with THE, from among the globally most famous ranking systems. Combining THE and Iran`s ranking systems could offer a far more global system which is capable of covering all aspects of ranking and universities` universal status. THE and ARWU can be named as the most complete combined systems, from among the global and most famous ranking systems, which can be used as a substitution for Iran`s ranking system

A Comparative Analysis between Global University Rankings and Environmental Sustainability of Universities

[EN] Global University Rankings (GURs) intend to measure the performance of universities worldwide. Other rankings have recently appeared that evaluate the creation of environmental policies in universities, e.g., the Universitas Indonesia (UI) GreenMetric. This work aims to analyze the interaction between the Top 500 of such rankings by considering the geographical location of universities and their typologies. A descriptive analysis and a statistical logistical regression analysis were carried out. The former demonstrated that European and North American universities predominated the Top 500 of GURs, while Asian universities did so in the Top 500 of the UI GreenMetric ranking, followed by European universities. Older universities predominated the Top 500 of GURs, while younger ones did so in the Top 500 of the UI GreenMetric ranking. The second analysis demonstrated that although Latin American universities were barely present in the Top 500 of GURs, the probability of them appearing in the Top 500 of the UI GreenMetric ranking was 5-fold. We conclude that a low association exists between universities' academic performance and their commitment to the natural environment in the heart of their institutions. It would be advisable for GURs to include environmental indicators to promote sustainability at universities and to contribute to climate change.Muñoz-Suárez, M.; Guadalajara Olmeda, MN.; Osca Lluch, JM. (2020). A Comparative Analysis between Global University Rankings and Environmental Sustainability of Universities. Sustainability. 12(14):1-19. https://doi.org/10.3390/su12145759S1191214Dill, D. D., & Soo, M. (2005). Academic quality, league tables, and public policy: A cross-national analysis of university ranking systems. 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Sustainability, 11(14), 3766. doi:10.3390/su11143766Academic Ranking of World Universities-ARWUhttp://www.shanghairanking.com/ARWU-Methodology-2017.htmlQS Top University Rankingshttps://www.topuniversities.com/qs-world-university-rankings/methodologyTHE World University Rankingshttps://www.timeshighereducation.com/world-university-rankingsRanking Web de Universidades-Webometricshttp://www.webometrics.info/en/About_UsLiu, Z., Moshi, G. J., & Awuor, C. M. (2019). Sustainability and Indicators of Newly Formed World-Class Universities (NFWCUs) between 2010 and 2018: Empirical Analysis from the Rankings of ARWU, QSWUR and THEWUR. Sustainability, 11(10), 2745. doi:10.3390/su11102745Marginson, S. (2013). University Rankings and Social Science. European Journal of Education, 49(1), 45-59. doi:10.1111/ejed.12061Hauptman Komotar, M. (2019). Global university rankings and their impact on the internationalisation of higher education. European Journal of Education, 54(2), 299-310. doi:10.1111/ejed.12332Peters, M. A. (2017). Global university rankings: Metrics, performance, governance. Educational Philosophy and Theory, 51(1), 5-13. doi:10.1080/00131857.2017.1381472Hosier, M., & Hoolash, B. K. A. (2017). The effect of methodological variations on university rankings and associated decision-making and policy. Studies in Higher Education, 44(1), 201-214. doi:10.1080/03075079.2017.1356282Safón, V. (2019). Inter-ranking reputational effects: an analysis of the Academic Ranking of World Universities (ARWU) and the Times Higher Education World University Rankings (THE) reputational relationship. Scientometrics, 121(2), 897-915. doi:10.1007/s11192-019-03214-9Tuesta, E. F., Garcia-Zorita, C., Ayllon, R. R., & Sanz-Casado, E. (2019). Does a Country/Region’s Economic Status Affect Its Universities’ Presence in International Rankings? Journal of Data and Information Science, 4(2), 56-78. doi:10.2478/jdis-2019-0009Dobrota, M., & Dobrota, M. (2015). ARWU ranking uncertainty and sensitivity: What if the award factor was Excluded? Journal of the Association for Information Science and Technology, 67(2), 480-482. doi:10.1002/asi.23527Dowsett, L. (2020). Global university rankings and strategic planning: a case study of Australian institutional performance. Journal of Higher Education Policy and Management, 42(4), 478-494. doi:10.1080/1360080x.2019.1701853Rehman, M. A., Kashif, M., & Mingione, M. (2017). Corporate Social Responsibility and Sustainability (CSRS) Initiatives among European and Asian Business Schools: A Web-based Content Analysis. Global Business Review, 20(5), 1231-1247. doi:10.1177/0972150917737435Doğan, G., & Al, U. (2019). Is it possible to rank universities using fewer indicators? A study on five international university rankings. Aslib Journal of Information Management, 71(1), 18-37. doi:10.1108/ajim-05-2018-0118Siniksaran, E., & Satman, M. H. (2019). WURS: a simulation software for university rankings—software review. Scientometrics, 122(1), 701-717. doi:10.1007/s11192-019-03269-8Çakır, M. P., Acartürk, C., Alaşehir, O., & Çilingir, C. (2015). A comparative analysis of global and national university ranking systems. Scientometrics, 103(3), 813-848. doi:10.1007/s11192-015-1586-6Docampo, D., & Cram, L. (2016). Academic performance and institutional resources: a cross-country analysis of research universities. Scientometrics, 110(2), 739-764. doi:10.1007/s11192-016-2189-6Jöns, H., & Hoyler, M. (2013). Global geographies of higher education: The perspective of world university rankings. Geoforum, 46, 45-59. doi:10.1016/j.geoforum.2012.12.014UI GreenMetric World University Rankinghttp://greenmetric.ui.ac.id/Suwartha, N., & Sari, R. F. (2013). Evaluating UI GreenMetric as a tool to support green universities development: assessment of the year 2011 ranking. Journal of Cleaner Production, 61, 46-53. doi:10.1016/j.jclepro.2013.02.034Lauder, A., Sari, R. F., Suwartha, N., & Tjahjono, G. (2015). Critical review of a global campus sustainability ranking: GreenMetric. Journal of Cleaner Production, 108, 852-863. doi:10.1016/j.jclepro.2015.02.080Ragazzi, M., & Ghidini, F. (2017). Environmental sustainability of universities: critical analysis of a green ranking. Energy Procedia, 119, 111-120. doi:10.1016/j.egypro.2017.07.054Marrone, P., Orsini, F., Asdrubali, F., & Guattari, C. (2018). Environmental performance of universities: Proposal for implementing campus urban morphology as an evaluation parameter in Green Metric. Sustainable Cities and Society, 42, 226-239. doi:10.1016/j.scs.2018.07.012Drahein, A. D., De Lima, E. P., & Da Costa, S. E. G. (2019). 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Self-defined information indices: application to the case of university rankings

[EN] University rankings are now relevant decision-making tools for both institutional and private purposes in the management of higher education and research. However, they are often computed only for a small set of institutions using some sophisticated parameters. In this paper we present a new and simple algorithm to calculate an approximation of these indices using some standard bibliometric variables, such as the number of citations from the scientific output of universities and the number of articles per quartile. To show our technique, some results for the ARWU index are presented. From a technical point of view, our technique, which follows a standard machine learning scheme, is based on the interpolation of two classical extrapolation formulas for Lipschitz functions defined in metric spaces-the so-called McShane and Whitney formulae-. In the model, the elements of the metric space are the universities, the distances are measured using some data that can be extracted from the Incites database, and the Lipschitz function is the ARWU index.The third and fourth authors gratefully acknowledge the support of the Ministerio de Ciencia, Innovacion y Universidades (Spain), Agencia Estatal de Investigacion, and FEDER, under Grant MTM2016-77054-C2-1-P. The first author gratefully acknowledge the support of Catedra de Transparencia y Gestion de Datos, Universitat Politecnica de Valencia y Generalitat Valenciana, Spain.Ferrer Sapena, A.; Erdogan, E.; Jiménez-Fernández, E.; Sánchez Pérez, EA.; Peset Mancebo, MF. (2020). Self-defined information indices: application to the case of university rankings. Scientometrics. 124(3):2443-2456. https://doi.org/10.1007/s11192-020-03575-6S244324561243Aguillo, I., Bar-Ilan, J., Levene, M., & Ortega, J. (2010). Comparing university rankings. Scientometrics, 85(1), 243–256.Asadi, K., Dipendra, M., & Littman, M. L. (2018). Lipschitz continuity in model-based reinforcement learning. In Proceedings of the 35th International Conference on Machine Learning, Proc. Mach. Lear. Res., Vol. 80, pp. 264–273.Bougnol, M. L., & Dulá, J. H. (2013). A mathematical model to optimize decisions to impact multi-attribute rankings. Scientometrics, 95(2), 785–796.Çakır, M. P., Acartürk, C., Alaşehir, O., & Çilingir, C. (2015). A comparative analysis of global and national university ranking systems. Scientometrics, 103(3), 813–848.Cancino, C. A., Merigó, J. M., & Coronado, F. C. (2017). A bibliometric analysis of leading universities in innovation research. Journal of Innovation & Knowledge, 2(3), 106–124.Chen, K.-H., & Liao, P.-Y. (2012). A comparative study on world university rankings: A bibliometric survey. Scientometrics, 92(1), 89–103.Cinzia, D., & Bonaccorsi, A. (2017). Beyond university rankings? Generating new indicators on universities by linking data in open platforms. Journal of the Association for Information Science and Technology, 68(2), 508–529.Cobzaş, Ş., Miculescu, R., & Nicolae, A. (2019). Lipschitz functions. Berlin: Springer.Deza, M. M., & Deza, E. (2009). Encyclopedia of distances. Berlin: Springer.2019 U-Multirank ranking: European universities performing well. https://ec.europa.eu/education/news/u-multirank-publishes-sixth-edition-en .Dobrota, M., Bulajic, M., Bornmann, L., & Jeremic, V. (2016). A new approach to the QS university ranking using the composite I-distance indicator: Uncertainty and sensitivity analyses. Journal of the Association for Information Science and Technology, 67(1), 200–211.Falciani, H., Calabuig, J. M., & Sánchez Pérez, E. A. (2020). Dreaming machine learning: Lipschitz extensions for reinforcement learning on financial markets. Neurocomputing, 398, 172–184.Kehm, B. M. (2014). 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Boosted ranking models: A unifying framework for ranking predictions. Knowledge and Information Systems, 30(3), 543–568.Saisana, M., d’Hombres, B., & Saltelli, A. (2011). Rickety numbers: Volatility of university rankings and policy implications. Research Policy, 40(1), 165–177.Tabassum, A., Hasan, M., Ahmed, S., Tasmin, R., Abdullah, D. M., & Musharrat, T. (2017). University ranking prediction system by analyzing influential global performance indicators. In 2017 9th International Conference on Knowledge and Smart Technology (KST) (pp. 126–131) IEEE.Van Raan, A. F. J., Van Leeuwen, T. N., & Visser, M. S. (2011). Severe language effect in university rankings: Particularly Germany and France are wronged in citation-based rankings. Scientometrics, 88(2), 495–498.von Luxburg, U., & Bousquet, O. (2004). Distance-based classification with Lipschitz functions. Journal of Machine Learning Research, 5, 669–695

College and University Ranking Systems: Global Perspectives and American Challenges

Examines how higher education ranking systems function, how other countries use ranking systems, and the impact of college rankings in the United States on student access, choice, and opportunity

Comparing nuclear power trajectories in Germany and the UK: from ‘regimes' to ‘democracies’ in sociotechnical transitions and Discontinuities

This paper focuses on arguably the single most striking contrast in contemporary major energy politics in Europe (and even the developed world as a whole): the starkly differing civil nuclear policies of Germany and the UK. Germany is seeking entirely to phase out nuclear power by 2022. Yet the UK advocates a ‘nuclear renaissance’, promoting the most ambitious new nuclear construction programme in Western Europe.Here,this paper poses a simple yet quite fundamental question: what are the particular divergent conditions most strongly implicated in the contrasting developments in these two countries. With nuclear playing such an iconic role in historical discussions over technological continuity and transformation, answering this may assist in wider understandings of sociotechnical incumbency and discontinuity in the burgeoning field of‘sustainability transitions’. To this end, an ‘abductive’ approach is taken: deploying nine potentially relevant criteria for understanding the different directions pursued in Germany and the UK. Together constituted by 30 parameters spanning literatures related to socio-technical regimes in general as well as nuclear technology in particular, the criteria are divided into those that are ‘internal’ and ‘external’ to the ‘focal regime configuration’ of nuclear power and associated ‘challenger technologies’ like renewables. It is ‘internal’ criteria that are emphasised in conventional sociotechnical regime theory, with ‘external’ criteria relatively less well explored. Asking under each criterion whether attempted discontinuation of nuclear power would be more likely in Germany or the UK, a clear picture emerges. ‘Internal’ criteria suggest attempted nuclear discontinuation should be more likely in the UK than in Germany– the reverse of what is occurring. ‘External’ criteria are more aligned with observed dynamics –especially those relating to military nuclear commitments and broader ‘qualities of democracy’. Despite many differences of framing concerning exactly what constitutes ‘democracy’, a rich political science literature on this point is unanimous in characterising Germany more positively than the UK. Although based only on a single case,a potentially important question is nonetheless raised as to whether sociotechnical regime theory might usefully give greater attention to the general importance of various aspects of democracy in constituting conditions for significant technological discontinuities and transformations. If so, the policy implications are significant. A number of important areas are identified for future research, including the roles of diverse understandings and specific aspects of democracy and the particular relevance of military nuclear commitments– whose under-discussion in civil nuclear policy literatures raises its own questions of democratic accountability

Ranking of palliative care development in the countries of the European Union

Context There is growing interest in monitoring palliative care (PC) development internationally. One aspect of this is the ranking of such development for comparative purposes. Objectives To generate a ranking classification and to compare scores for PC development in the countries of the European Union (EU), 2007 and 2013. PC “development” in this study is understood as a combination of the existence of relevant services in a country (“resources”) plus the capacity to develop further resources in the future (“vitality”). Methods “Resources” comprise indicators of three types of PC services per population (inpatient palliative care units and inpatient hospices [IPCU], hospital support teams [HST] and home care teams [HCT]). “Vitality” of PC is estimated by numerical scores for the existence of a national association, a directory of services, physician accreditation, attendances at a key European conference and volume of publications on PC development. The leading country (by raw score) is then considered as the reference point against which all other countries are measured. Different weightings are applied to resources (75%) and vitality (25%). From this, an overall ranking is constructed. Results The U.K. achieved the highest level of development (86% of the maximum possible score), followed by Belgium and The Netherlands (81%), and Sweden (80%). In the domain resources, Luxembourg, the U.K. and Belgium were leading. The top countries in vitality were Germany and the U.K. In comparison to 2007, The Netherlands, Malta and Portugal showed the biggest improvements, whereas the positions of Spain, France and Greece deteriorated. Conclusion The ranking method permitted a comparison of palliative care development between countries and shows changes over time. Recommendations for improving the ranking include improvements to the methodology and greater explanation of the levels and changes it reveals