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Merton proposed middle range theories, not as ends in themselves, but as bases for a consolidation to explain broader phenomenon. More than a half century has passed since Merton's consolidation proposal and in that time a number of experimentally tested middle range theories have been developed. Certainly, the next step should be a consolidation. Yet, to our knowledge, no one has previously offered a consolidation of experimentally tested theories and applied it for explanation. This paper offers a consolidation of middle range theories formulated to explain the rise of the pristine state. Two theories of this consolidation, Status Characteristics Theory and Elementary Theory, form the core of what Analytic Sociologists have called a toolbox of theories. Our toolbox forms an integrated consolidation in two ways. First, the social structures modeled by the theories form a path-dependent process of increasing benefits gained by the elites of the structures. Second, the end conditions of each step of the process are the initial conditions of the next. Whereas the theories in our toolbox have previously been seen as applying exclusively to microstructures, we have encountered no difficulties in scaling them up to apply to macrostructures. While we hope the consolidation has validity in explaining the occurrence of pristine states, this paper's significance lies in its demonstration that today social theories can be consolidated and applied for explanation. [ABSTRACT FROM AUTHOR]

The paper offers a distinctive reading of Popper’s work, suggesting that his Logic of Scientific Discovery (LScD) might be re-interpreted in the light of his Open Society. Indeed, Popper can be interpreted as criticising certain aspects of his first book, and as a result improving upon them, in his second. It suggests translating what Popper says about ‘conventions’ into his later vocabulary of ‘social institutions’. Looking back, I believe that Popper never intended the language of conventions and decisions to be read individualistically. I remain unsure whether Popper was himself quite as clear about this as he could have been. My reading makes Popper a pioneer in the sociology of science. Scientific institutions are arenas of political power; but Popper did not discuss the structure and inter-relations of the social institutions of science, or offer a politics of science in the context of his methodology. What is missing from the skeletal sociology of LScD is the politics. We could put it in Popperian terms this way: scientific institutions are both open and closed. They are closed, firmly, to the inexpert, to the nonmembers; supposedly they are open to the qualified, provided the prerogatives of seniority and leadership are acknowledged. Despite these shortcomings, Popper’s critical and rational approach and his insistence on openness and intellectual honesty are still important today. [ABSTRACT FROM AUTHOR]

This paper exposes a theoretical approach proposal for the analysis of social inequalities through the notions of socialization and subjective experience, from a comprehensive sociology and Schutz's phenomenological perspective. The intention is to emphasize the subjective dimension in the phenomenon of inequality, which refers to the naturalization that occurs through the mental categories that we use to look at and build the social world, and that results in an aspect that strongly adds to structural conditioning of this problem. In particular, it is proposed to study this phenomenon in the context of high school, since it represents a particular period of life in which teenagers appropriate identity categories and in relation to the social order in which they operate. The results, from this approach, propose to rescue the role of the educational institution in the subjective incorporation of this problem, and, with it, address school practices and their objective effects on the lives of students. [ABSTRACT FROM AUTHOR]

In this paper, I investigate a connection between a common characterisation of freedom and how uncertainty is managed in a Bayesian hierarchical model. To do this, I consider a distributed factorization of a group's optimization of free energy, in which each agent is attempting to align with the group and with its own model. I show how this can lead to equilibria for groups, defined by the capacity of the model being used, essentially how many different datasets it can handle. In particular, I show that there is a "sweet spot" in the capacity of a normal model in each agent's decentralized optimization, and that this "sweet spot" corresponds to minimal free energy for the group. At the sweet spot, an agent can predict what the group will do and the group is not surprised by the agent. However, there is an asymmetry. A higher capacity model for an agent makes it harder for the individual to learn, as there are more parameters. Simultaneously, a higher capacity model for the group, implemented as a higher capacity model for each member agent, makes it easier for a group to integrate a new member. To optimize for a group of agents then requires one to make a trade-off in capacity, as each individual agent seeks to decrease capacity, but there is pressure from the group to increase capacity of all members. This pressure exists because as individual agent's capacities are reduced, so too are their abilities to model other agents, and thereby to establish pro-social behavioural patterns. I then consider a basic two-level (dual process) Bayesian model of social reasoning and a set of three parameters of capacity that are required to implement such a model. Considering these three capacities as dependent elements in a free energy minimization for a group leads to a "sweet surface" in a three-dimensional space defining the triplet of parameters that each agent must use should they hope to minimize free energy as a group. Finally, I relate these three parameters to three notions of freedom and equality in human social organization, and postulate a correspondence between freedom and model capacity. That is, models with higher capacity, have more freedom as they can interact with more datasets. [ABSTRACT FROM AUTHOR]