Ranking dynamics and volatility

Scientific journals are ordered by their impact factor while countries, institutions or researchers can be ranked by their scientific production, impact or by other simple or composite indicators as in the case of university rankings. In this paper, the theoretical framework proposed in Criado, R., Garcia, E., Pedroche, F. & Romance, M. (2013). A new method for comparing rankings through complex networks: Model and analysis of competitiveness of major European soccer leagues. Chaos, 23, 043114 for football competitions is used as a starting point to define a general index describing the dynamics or its opposite, stability, of rankings. Some characteristics to study rankings, ranking dynamics measures and axioms for such indices are presented. Furthermore, the notion of volatility of elements in rankings is introduced. Our study includes rankings with ties, entrants and leavers. Finally, some worked out examples are shown

Visegrad Group countries compared through world university rankings

The Visegrad Group is an alliance of four Central European countries: Czech Republic, Hungary, Poland, and Slovakia, founded by the Visegrad Declaration in 1991. The historical, political, and cultural similarities, highlighted by their shared experiences with economic transformation, make the Visegrad Group countries well suited for comparison. The article analyses and compares the performance of Visegrad Four (V4) countries in the recent editions of the most established individual university rankings as well as in the recent rankings of national higher education systems. Czech Republic ranks highest, followed by Poland and Hungary at approximately the same level, while Slovakia falls behind other V4 countries. Relevant socioeconomic factors influencing the country’s performance in university rankings are considered and discussed. The results confirm the leading position of the Czech Republic in the region, and they are in line with the recently conducted studies comparing the economic attributes, RD expenditures and quality of life in the V4 countries. The results thus also prove and confirm the strong interconnection between the economic performance, RD expenditures and the performance of the higher education sector

Evaluation of image features using a photorealistic virtual world

Image features are widely used in computer vision applications. They need to be robust to scene changes and image transformations. Designing and comparing feature descriptors requires the ability to evaluate their performance with respect to those transformations. We want to know how robust the descriptors are to changes in the lighting, scene, or viewing conditions. For this, we need ground truth data of different scenes viewed under different camera or lighting conditions in a controlled way. Such data is very difficult to gather in a real-world setting. We propose using a photorealistic virtual world to gain complete and repeatable control of the environment in order to evaluate image features. We calibrate our virtual world evaluations by comparing against feature rankings made from photographic data of the same subject matter (the Statue of Liberty). We find very similar feature rankings between the two datasets. We then use our virtual world to study the effects on descriptor performance of controlled changes in viewpoint and illumination. We also study the effect of augmenting the descriptors with depth information to improve performance.Quanta Computer (Firm)Shell ResearchUnited States. Office of Naval Research. Multidisciplinary University Research Initiative (Grant N00014-06-1-0734)United States. Office of Naval Research. Multidisciplinary University Research Initiative. CAREER (Award Number 0747120)United States. Office of Naval Research. Multidisciplinary University Research Initiative (Grant N000141010933)Microsoft CorporationAdobe SystemsGoogle (Firm

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). Global university rankings—Impacts and unintended side effects. European Journal of Education, 49(1), 102–112.Lim, M. A., & Øerberg, J. W. (2017). Active instruments: On the use of university rankings in developing national systems of higher education. Policy Reviews in Higher Education, 1(1), 91–108.Luo, F., Sun, A., Erdt, M., Raamkumar, A. S., & Theng, Y. L. (2018). Exploring prestigious citations sourced from top universities in bibliometrics and altmetrics: A case study in the computer science discipline. Scientometrics, 114(1), 1–17.Marginson, S. (2014). University rankings and social science. European Journal of Education, 49(1), 45–59.Pagell, R. A. (2014). Bibliometrics and university research rankings demystified for librarians. Library and information sciences (pp. 137–160). Berlin: Springer.Rao, A. (2015). Algorithms for Lipschitz extensions on graphs. Yale University: ProQuest Dissertations Publishing, 10010433.Rosa, K. D., Metsis, V., & Athitsos, V. (2012). 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

Informational analysis of international university rankings

This work on the informational analysis of international rankings is motivated by the fact that international university rankings are increasing their impact and importance. Given their diversity of origins, purposes and procedures, it makes sense to try to increase their knowledge and understanding by clearly defining and comparing them from an informational stance. This research-orientated master thesis (master final project or TFM) addresses this purpose, by aiming at a clear and comparative definition of both the information managed by those rankings, as well as their respective processes for capturing, processing and publishing their results. These comparative definitions are carried using the Method for informational analysis of university rankings derived and designed from the experience of pursing the analysis work within this master thesis. At the same time, this method allows to assess transparency on rankings and helps to clarify the focus of the information used for rank universities

Students\u27 Perception Index of the MUGC School Psychology Practicum : A Correlation of Course Work with Practicum Experiences

The perceived consistency between the course work and practicum of the Marshall University Graduate College school psychology program was investigated. The magnitude of consistency was determined by a Spearman correlation coefficient comparing students’ subjective rankings of the relative importance of various school psychologist activities as emphasized in the program’s course work with the relative importance of those same activities as relevant to the practicum setting. The findings suggest a significant degree of correlation at the (.05) significance level between what is emphasized in the program’s coursework and what is relevant to practicum experience

University Autonomy Decline

This book provides empirically grounded insights into the causes, trajectories, and effects of a severe decline in university autonomy and the relationship to other dimensions of academic freedom by comparing in-depth country studies and evidence from a new global timeseries dataset. Drawing attention to ongoing discussions on standards for monitoring and assessment of academic freedom at regional and international organizations, this book identifies a need for clearer standards on academic freedom and a human rights-based definition of university autonomy. Further, the book calls for accompanying international oversight and the inclusion of criteria related to academic freedom in international university rankings. Five expert-authored case studies on academic freedom from diverse nations (Bangladesh, Mozambique, India, Poland, and Turkey) are included in the volume. Drawing on both qualitative and quantitative evidence, the book offers a unique and timely contribution to the field and will be of great interest to scholars, researchers, and students in the fields of higher education, human rights, political science and public policy