Tom Griffiths

This book re-examines aspects of historical socialism, and includes case studies of education within twenty-first century socialist and post-socialist contexts shaped by the trajectories of historical socialism. Through these case studies, contributions offer insights into key questions: How are education systems and student subjectivities shaped by post-socialist trajectories and current regional politics, economics and resistance movements? How do sedimented socialist discourses and geographies alter and contest the ‘neoliberal child’ and ‘childhood’ in post-socialist education? How have disjunctures between the rhetoric of historical Marxism-Leninism and the practices of educators, students and student political organizations played out under socialism, and what could we learn from that for our present? How much emancipatory potential is there in the theories and practices of education for combatting injustice in the absence of mass, revolutionary political parties? Above all, this volume affirms the need to move beyond simplistic accounts of historical socialism and post-socialist transitions. By exploring how socialist trajectories remain influential and have potential in our current contexts, this book contributes to the work of politically engaged educators working to re-imagine and reconstruct education. This book was originally published as a special issue of _Globalisation, Societies and Education. _

Immanuel Wallerstein and István Mészáros are prolific scholars whose analyses of global capitalism in crisis offer distinctive insight for research across the social sciences. This book engages readers with their main theses, encouraging the application of these in our analysis of social reality and as its mass educational institutions. Griffiths and Imre undertake this task in their presentation of work under the capitalist world-economy, and the official function of mass education to prepare workers for the global economy. They develop a timely and provocative critique of mass education for this century, challenging readers to contribute to the construction of radical alternatives

Watching another person take actions to complete a goal and making inferences about that person's knowledge is a relatively natural task for people. This ability can be especially important in educational settings, where the inferences can be used for assessment, diagnosing misconceptions, and providing informative feedback. In this paper, we develop a general framework for automatically making such inferences based on observed actions; this framework is particularly relevant for inferring student knowledge in educational games and other interactive virtual environments. Our approach relies on modeling action planning: We formalize the problem as a Markov decision process in which one must choose what actions to take to complete a goal, where choices will be dependent on one's beliefs about how actions affect the environment. We use a variation of inverse reinforcement learning to infer these beliefs. Through two lab experiments, we show that this model can recover people's beliefs in a simple environment, with accuracy comparable to that of human observers. We then demonstrate that the model can be used to provide real-time feedback and to model data from an existing educational game

Languages are transmitted from person to person and generation to generation via a process of iterated learning: people learn a language from other people who once learned that language themselves. We analyze the consequences of iterated learning for learning algorithms based on the principles of Bayesian inference, assuming that learners compute a posterior distribution over languages by combining a prior (representing their inductive biases) with the evidence provided by linguistic data. We show that when learners sample languages from this posterior distribution, iterated learning converges to a distribution over languages that is determined entirely by the prior. Under these conditions, iterated learning is a form of Gibbs sampling, a widely-used Markov chain Monte Carlo algorithm. The consequences of iterated learning are more complicated when learners choose the language with maximum posterior probability, being affected by both the prior of the learners and the amount of information transmitted between generations. We show that in this case, iterated learning corresponds to another statistical inference algorithm, a variant of the expectation-maximization (EM) algorithm. These results clarify the role of iterated learning in explanations of linguistic universals and provide a formal connection between constraints on language acquisition and the languages that come to be spoken, suggesting that information transmitted via iterated learning will ultimately come to mirror the minds of the learners

Bayesian Rationality (Oaksford & Chater 2007) illustrates the strengths of Bayesian models of cognition: the systematicity of rational explanations, transparent assumptions about human learners, and combining structured symbolic representation with statistics. However, the book also highlights some of the challenges this approach faces: of providing psychological mechanisms, explaining the origins of the knowledge that guides human learning, and accounting for how people make genuinely new discoveries

Shepard has argued that a universal law should govern generalization across different domains of perception and cognition, as well as across organisms from different species or even different planets. Starting with some basic assumptions about natural kinds, he derived an exponential decay function as the form of the universal generalization gradient, which accords strikingly well with a wide range of empirical data. However, his original formulation applied only to the ideal case of generalization from a single encountered stimulus to a single novel stimulus, and for stimuli that can be represented as points in a continuous metric psychological space. Here we recast Shepard's theory in a more general Bayesian framework and show how this naturally extends his approach to the more realistic situation of generalizing from multiple consequential stimuli with arbitrary representational structure. Our framework also subsumes a version of Tversky's set-theoretic model of similarity, which is conventionally thought of as the primary alternative to Shepard's continuous metric space model of similarity and generalization. This unification allows us not only to draw deep parallels between the set-theoretic and spatial approaches, but also to significantly advance the explanatory power of set-theoretic models. Key Words: additive clustering; Bayesian inference; categorization; concept learning; contrast model; features; generalization; psychological space; similarity.

George Seddon takes a cheeky pride in his native wit, in his ability to improvise, invent, and to trip lightly over difficult terrain. These are the bush virtues of the Man from Snowy River. In this essay I reflect upon the interdisciplinary (and undisciplined) nature of Seddon's vision and practice, and place him in a tradition of nature and landscape writing in Australia that goes back to the 19th century. But I also suggest that he has been ahead of his time in many ways, particularly in his anticipation of the field of environmental history. He has been one of the people who has generated new environmental narratives that indigenize Australia, and which undermine the dominant imperialist, diffusionist, one-way accounts of origins. In this sense, Seddon has creatively embraced - and sometimes overturned - the predicament of the antipodean. Seddon happily combines poetry and practice. He became known as the `powerline man' in landscape planning, and enjoyed quoting William Blake to the State Electricity Commission of Victoria in his report on the Loy Yang coal field development. By analysing his 1998 submission to the Snowy Water Inquiry, the essay shows how Seddon unites `good science, good planning, good design, and good communication', and offers environmental wisdom that is both highly intellectual and extremely practical