Дифференциация в высшем образовании: основные концепции и подходы к изучению
Global rankings and the Geopolitics of Higher Education is an examination of the impact and influence that university rankings have had on higher education, policy and public opinion in recent years. Bringing together some of the most informed authorities on this very complex issue, this edited collection of specially commissioned chapters examines the changes affecting higher education and the implications for society and the economy.
Split into four interrelated sections, this book covers:The development of rankings in higher education, how they have impacted upon both the production of knowledge and its geography, and their influence in shaping policymaking. Overviews of the significance of rankings for higher education systems in Europe, Asia, Africa, Russia, South America, India and North America. An analysis of rankings in relation to key concerns that pervade contemporary higher education. Examination of the role rankings are likely to play in the future directions for higher education.
This is a significant scholarly work that analyses in depth an important development in higher education systems, and which is likely to have an important influence upon how we understand the higher education policy-making process – past, present and future. It provides new analysis and conceptual understanding for researchers, and firm evidence for policy makers to use when addressing the value of rankings in measuring the quality of their institutions. Besides bringing together a powerful cast of academics, this book incorporates contributions from heads of important international higher education organisations – from both those involved in making and also in administering key decisions.
This timely, reflective and accessible book forms crucial reading for those studying the subject of rankings, as well as the broader implications and unintended consequences of rankings on national higher education policies. Extending beyond academic researchers and students, this book will also be of significant interest to policymakers, higher education leaders and key stakeholders.
The paper makes a brief introduction into multiple classifier systems and describes a particular algorithm which improves classification accuracy by making a recommendation of an algorithm to an object. This recommendation is done under a hypothesis that a classifier is likely to predict the label of the object correctly if it has correctly classified its neighbors. The process of assigning a classifier to each object involves here the apparatus of Formal Concept Analysis. We explain the principle of the algorithm on a toy example and describe experiments with real-world datasets.
Symbolic classifiers allow for solving classification task and provide the reason for the classifier decision. Such classifiers were studied by a large number of researchers and known under a number of names including tests, JSM-hypotheses, version spaces, emerging patterns, proper predictors of a target class, representative sets etc. Here we consider such classifiers with restriction on counter-examples and discuss them in terms of pattern structures. We show how such classifiers are related. In particular, we discuss the equivalence between good maximally redundant tests and minimal JSM-hyposethes and between minimal representations of version spaces and good irredundant tests.
This book constitutes the refereed proceedings of the 6th IAPR TC3 International Workshop on Artificial Neural Networks in Pattern Recognition, ANNPR 2014, held in Montreal, QC, Canada, in October 2014. The 24 revised full papers presented were carefully reviewed and selected from 37 submissions for inclusion in this volume. They cover a large range of topics in the field of learning algorithms and architectures and discussing the latest research, results, and ideas in these areas.
In this paper, we use robust optimization models to formulate the support vector machines (SVMs) with polyhedral uncertainties of the input data points. The formulations in our models are nonlinear and we use Lagrange multipliers to give the first-order optimality conditions and reformulation methods to solve these problems. In addition, we have proposed the models for transductive SVMs with input uncertainties.
The article is based on the results of qualitative interviews and analysis of documents. The authors consider conditions of the development of social anthropology curricula in Russian universities. They claim that social anthropology programs in Russia in the beginning of 1990s have been established under the conditions of competition of different agents and their ideologies. The study of a discussion on educational standards helps reconstruct institutional dynamics that have led to a crisis of university training program in social anthropology. An analytic perspective of sociology of knowledge has been used to consider such factors of this program development as legacy of intellectual traditions, ideological and bureaucratic control of higher education, conflict of agents interested in monopolization of this field. The types of educational programs have been presented that implement national standard in social anthropology in different Russian universities. The typology is based on the axes universal / local and pure / applied scholarship.
Institutions affect investment decisions, including investments in human capital. Hence institutions are relevant for the allocation of talent. Good market-supporting institutions attract talent to productive value-creating activities, whereas poor ones raise the appeal of rent-seeking. We propose a theoretical model that predicts that more talented individuals are particularly sensitive in their career choices to the quality of institutions, and test these predictions on a sample of around 95 countries of the world. We find a strong positive association between the quality of institutions and graduation of college and university students in science, and an even stronger negative correlation with graduation in law. Our findings are robust to various specifications of empirical models, including smaller samples of former colonies and transition countries. The quality of human capital makes the distinction between educational choices under strong and weak institutions particularly sharp. We show that the allocation of talent is an important link between institutions and growth.