Identifying Essential Epistemic Heuristics for Guiding Mechanistic Reasoning in Science Learning

Mechanistic reasoning, or reasoning systematically through underlying factors and relationships that give rise to phenomena, is a powerful thinking strategy that allows one to explain and make predictions about phenomena. This article synthesizes and builds on existing frameworks to identify essential characteristics of students’ mechanistic reasoning across scientific content areas. We argue that these characteristics can be represented as epistemic heuristics, or ideas about how to direct one’s intellectual work, that implicitly guide mechanistic reasoning. We use this framework to characterize middle school students’ written explanatory accounts of two phenomena in different science content areas using these heuristics. We demonstrate evidence of heuristics in students’ accounts and show that the use of the heuristics was related to but distinct from science content knowledge. We describe how the heuristics allowed us to characterize and compare the mechanistic sophistication of account construction across science content areas. This framework captures elements of a crosscutting practical epistemology that may support students in directing the construction of mechanistic accounts across content areas over time, and it allows us to characterize that progress.     

What exactly are technological regimes?: Intra-industry heterogeneity in the organization of innovation activities

This empirical study analyzes the patterns of innovation within and across industries using firm-level survey data from Finland and Denmark. The theoretical starting point is evolutionary theory with its premise that firms in different technological regimes pursue different paths to innovation. Similar modes of behaviour are found in the two datasets, and they closely correspond to those found in earlier studies. Contrary to prevailing assumptions, however, the results show that industries are not at all uniform in terms of how firms innovate; in almost all four- or five-digit NACE industries, three or more different modes of innovation can be identified. This suggests that firms’ strategic differentiation or local search activities overcome pressures in the technological environment towards homogenous behaviour, at least in the short term.     

Risk of Early Onset Substance Use among Students with and without Mild Academic Disabilities: Results of a Discrete‐Time Survival Analysis

This study investigated the age of onset of substance use among 536 students with mild academic disabilities and 906 students without academic disabilities, and the extent to which emotional, conduct, and hyperactivity problems explain the differences between these two groups. Using discrete‐time survival analysis, the results of this study showed that students with a mild academic disability were not at higher risk for the onset of daily smoking, and were at a significantly lower risk for the onset of weekly alcohol use and cannabis use. Though students with a mild academic disability displayed higher scores on conduct, emotional, and hyperactivity problems compared with their counterparts without an academic disability, this was not reflected in an increased risk for early onset substance use.     

Bottom-up estimation of industrial and public R&D investment by technology in support of policy-making: The case of selected low-carbon energy technologies

This paper discusses a bottom-up approach to estimate the level of R&D investment by technology in areas where data are scarce. It develops a four-step methodology for the estimation of corporate R&D investments at technology level. This approach can overcome gaps in existing data by combining publicly available information in a novel way, even though it introduces some uncertainty. This is illustrated for a set of low-carbon energy technologies that were identified as key for meeting Europe's long-term energy and climate objectives by the European Strategic Energy Technology Plan. The paper finds that the aggregated R&D investments dedicated to these technologies amounted to €3.3 billion in the EU in 2007, including public funding from European Union Member States and at EU-level, and industrial research activities from companies with headquarters registered in the EU. The results allow conclusions on the European energy research policy to be drawn, such as the dominance of industrial funds, and have provided significant input to the European policy making in this field. The paper ends with suggestions on how to further enhance the accuracy of the approach and how to widen its application to other sectors.