Former Socialist Economies and the Undergraduate Curriculum

The authors report on the results of a survey of current undergraduate instruction on the socialist economic system and post-socialist economies. Based on responses from eighty colleges and universities, they evaluate how course offerings and content have changed in light of the momentous developments of the past decade. The evidence is then used to comment on trends and potential future developments in classes on comparative economic systems and transition economies. Although undergraduate offerings in these areas have arrived at a short-run equilibrium, there are good reasons to believe that the structure of the courses should soon be re-thought.college teaching, socialist systems, transition economies

Household Savings in Russia during the Transition

We exploit panel data from the second phase of the Russian Longitudinal Monitoring Survey (RLMS) to investigate the household characteristics that explain saving during a period of extreme dislocation. Among our more noteworthy findings, we find evidence of short-term consumption smoothing behavior as households respond to temporary income shocks. Conditional on income level, we find that savings rates are higher in households benefiting from non-standard (likely transitory) sources of support such as private transfers and sales of home produced food; savings rates are lower, moreover, in households suffering from unemployment or payment arrears. We also confirm the robustness of an atypical U-shaped age-savings relationship to multivariate specifications. And finally, we turn up strong support for an inverse relationship between the household’s stock of durables and its saving rate.

The Theoretical Argument for Disproving Asymptotic Upper-Bounds on the Accuracy of Part-of-Speech Tagging Algorithms: Adopting a Linguistics, Rule-Based Approach

This paper takes a deep dive into a particular area of the interdisciplinary domain of Computational Linguistics, Part-of-Speech Tagging algorithms. The author relies primarily on scholarly Computer Science and Linguistics papers to describe previous approaches to this task and the often-hypothesized existence of the asymptotic accuracy rate of around 98%, by which this task is allegedly bound. However, after doing more research into why the accuracy of previous algorithms have behaved in this asymptotic manner, the author identifies valid and empirically-backed reasons why the accuracy of previous approaches do not necessarily reflect any sort of general asymptotic bound on the task of automated Part-of-Speech Tagging. In response, a theoretical argument is proposed to circumvent the shortcomings of previous approaches to this task, which involves abandoning the flawed status-quo of training machine learning algorithms and predictive models on outdated corpora, and instead walks the reader from conception through implementation of a rule-based algorithm with roots in both practical and theoretical Linguistics. While the resulting algorithm is simply a prototype which cannot be currently verified in achieving a tagging-accuracy rate of over 98%, its multi-tiered methodology, meant to mirror aspects of human cognition in Natural Language Understanding, is meant to serve as a theoretical blueprint for a new and inevitably more-reliable way to deal with the challenges in Part-of-Speech Tagging, and provide much-needed advances in the popular area of Natural Language Processing

The Theoretical Argument for Disproving Asymptotic Upper-Bounds on the Accuracy of Part-of-Speech Tagging Algorithms: Adopting a Linguistics, Rule-Based Approach

This paper takes a deep dive into a particular area of the interdisciplinary domain of Computational Linguistics, Part-of-Speech Tagging algorithms. The author relies primarily on scholarly Computer Science and Linguistics papers to describe previous approaches to this task and the often-hypothesized existence of the asymptotic accuracy rate of around 98%, by which this task is allegedly bound. However, after doing more research into why the accuracy of previous algorithms have behaved in this asymptotic manner, the author identifies valid and empirically-backed reasons why the accuracy of previous approaches do not necessarily reflect any sort of general asymptotic bound on the task of automated Part-of-Speech Tagging. In response, a theoretical argument is proposed to circumvent the shortcomings of previous approaches to this task, which involves abandoning the flawed status-quo of training machine learning algorithms and predictive models on outdated corpora, and instead walks the reader from conception through implementation of a rule-based algorithm with roots in both practical and theoretical Linguistics. While the resulting algorithm is simply a prototype which cannot be currently verified in achieving a tagging-accuracy rate of over 98%, its multi-tiered methodology, meant to mirror aspects of human cognition in Natural Language Understanding, is meant to serve as a theoretical blueprint for a new and inevitably more-reliable way to deal with the challenges in Part-of-Speech Tagging, and provide much-needed advances in the popular area of Natural Language Processing

Voices of Change from Great Britain

SPEA Lecturer Bill Foley highlights students' experiences while studying national security in London during Brexit

Parenting practices and children's cognitive effort: a laboratory study

We examine the association between parenting practices (discipline and support) and children's cognitive effort. Cognitive effort is hard to measure; hence, little is known in general about effort dispositions, and in particular about the influence of parenting practices on effort. We present data from a study on almost 1,400 fifth grade students from Berlin and Madrid. Cognitive effort is measured with tests of executive function. The students do the tests under an unincentivised and incentivised condition. We study two effort-related outcomes: "effort direction" - the child's decision to voluntarily do a real-effort task &- and "effort intensity" - the child's performance on the task. Results indicate that both parental discipline and support are associated with effort direction and the presence of incentives moderates this association. However, only parental discipline is (weakly) associated with effort intensity. We conclude that parenting practices primarily influence deliberative rather than instinctual types of cognitive effort.This research has received funding from the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (grant agreement No 758600)