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Michael Weisberg's account of scientific models concentrates on the ways in which models are similar to their targets. He intends not merely to explain what similarity consists in, but also to capture similarity judgments made by scientists. In order to scrutinize whether his account fulfills this goal, I outline one common way in which scientists judge whether a model is similar enough to its target, namely maximum likelihood estimation method (MLE). Then I consider whether Weisberg's account could capture the judgments involved in this practice. I argue that his account fails for three reasons. First, his account is simply too abstract to capture what is going on in MLE. Second, it implies an atomistic conception of similarity, while MLE operates in a holistic manner. Third, Weisberg's atomistic conception of similarity can be traced back to a problematic set-theoretic approach to the structure of models. Finally, I tentatively suggest how these problems might be solved by a holistic approach in which models and targets are compared in a non-set-theoretic fashion.

The purpose of this article is to add to the collective body of knowledge on student behavior and attitudes relative to the adoption of digital textbooks. The article summarizes an ongoing research project that examines past, current and evolving behavior in the classroom related to digital textbooks and school. It includes students, faculty and administrative attitudes behaviors and perceptions. This research was undertaken at the Sawyer Business School of Suffolk University in Boston, Massachusetts. Student attitudes and behavior toward their use of digital textbooks (eTextbooks) in higher education was examined in an ongoing longitudinal study over two years at Sawyer Business School, Suffolk University. Students in the class were divided into six teams. Five of the teams were assigned an eTextbook device and the sixth team was given a paper textbook for use through the semester. The digital technologies examined were: Amazon Kindle, Sony eReader Touch, Apple iPad, enTourage eDGe, and CourseSmart. Student attitudes and behaviors were examined pre and post class by survey each semester, and during the semesters through quizzes, journals and classroom discussion. Differential learning was measured between the six teams. Student attitudes and behaviors are becoming more receptive to and accepting of using digital textbooks each year. There was no significant difference in learning between the eTextbook devices teams or between them and the paper textbook team.

This paper critically examines Weisberg’s weighted feature matching account of model-world similarity. A number of concerns are raised, including that Weisberg provides an account of what underlies scientific judgments of relative similarity, when what is desired is an account of the sorts of model-target similarities that are necessary or sufficient for achieving particular types of modeling goal. Other concerns relate to the details of the account, in particular to the content of feature sets, the nature of shared features and the assumed independence of feature weightings.

A scientific community can be modeled as a collection of epistemic agents attempting to answer questions, in part by communicating about their hypotheses and results. We can treat the pathways of scientific communication as a network. When we do, it becomes clear that the interaction between the structure of the network and the nature of the question under investigation affects epistemic desiderata, including accuracy and speed to community consensus. Here we build on previous work, both our own and others’, in order to get a firmer grasp on precisely which features of scientific communities interact with which features of scientific questions in order to influence epistemic outcomes. Here we introduce a measure on the landscape meant to capture some aspects of the difficulty of answering an empirical question. We then investigate both how different communication networks affect whether the community finds the best answer and the time it takes for the community to reach consensus on an answer. We measure these two epistemic desiderata on a continuum of networks sampled from the Watts–Strogatz spectrum. It turns out that finding the best answer and reaching consensus exhibit radically different patterns. The time it takes for a community to reach a consensus in these models roughly tracks mean path length in the network. Whether a scientific community finds the best answer, on the other hand, tracks neither mean path length nor clustering coefficient.

This article hopes to honor Richard Weisberg in the form of a דבר תרה (\"word of Torah\"), which, although typically a sermon upon the weekly reading of the Pentateuch, can also more broadly refer to - as here - a discourse about anything meaningfully Jewish. Beginning, and ending, with the question of whether deconstructionism is not Talmudic, it proceeds by, respecting the former, studiously avoiding (almost) anything but apophatic \"answers,\" except that, respecting the latter, it endeavors to blazon, by way of Richard's thoroughly Nietzschean geist concerning good versus bad readers of texts, his even more inveterately, imperative Jewish ethos.

The article defends and celebrates Richard Weisberg's current brief for intransigence, in part by revisiting his early and groundbreaking reading of Billy Budd, Sailor.

Michael Weisberg has argued that robustness analysis is useful in evaluating both scientific models and their implications and that robustness analysis comes in three types that share their form and aim. We argue for three cautionary claims regarding Weisberg’s reconstruction: (1) robustness analysis may be of limited or no value in evaluating models and their implications; (2) the unificatory reconstruction conceals that the three types of robustness differ in form and role; (3) there is no confluence of types of robustness. We illustrate our central first claim with a case study: the application of Lotka-Volterra models to technology diffusion.