The incompatibility of knowledge regimes: consequences of the material world for cross-domain work

In this paper, we argue that successful integration of knowledge across work domains in the short-term can mask the generation of long-term consequences. We explore a setting, the introduction of environmental considerations into semiconductor manufacturing, where the eventual adoption of common measurement artifacts and associated practices enabled knowledge integration, but failed to address significant underlying consequences. Drawing from observational, interview, and archival data we develop an understanding of the work practices of the Tech and EnviroTech groups as structured by the material world and broader collective conventions. We introduce the concept of knowledge regime to outline the differences in knowledge across these work domains. More specifically, we find that differences in the causal specificity and developmental time horizon of knowledge and the measurement artifacts that result contribute to the relative power of one knowledge regime over another. Understanding these sources of incompatibility provides insight into the design requirements of information systems as boundary objects for knowledge integration, but also specifies the potential limits to any design effort.     

An APL procedure to estimate genotype by environment interaction when two environments are considered

Genotype by environment interaction (GEI) is a relevant topic in many fields of applied biology. Normal parametric approaches following analysis of variance are not suitable when large differences between error variances within environments are present. We illustrate an APL program following Yamada's approach to estimate GEIs and ancillary statistics when two environments are considered. The choice of APL environment is based on its value as a useful tool in algorithm implementation and problem-solving. Its use is suggested in biological applications where matrix algebra is involved and an understanding of the computing complexity of problems is generally not required.     

Why is science difficult to learn? Things are seldom what they seem

Abstract The difficulties of learning science are related to the nature of science itself and to the methods by which science is customarily taught without regard to what is known about children's learning. An information processing model is proposed to guide thinking and research in this area.     

A cost-effective approach to testing

A method for assessing the differential risk of failure among a system's modules is proposed. The procedure has three components: external-risk assessment, module exposure, and module-failure likelihood. External-risk assessment is a consideration of the system's environment, almost independent of the software's details. To estimate module exposure, the model relates individual modules and their potential faults to the external-failure modes and their economic consequences by reverse-engineering the specifications and analyzing each module's expected use. To estimate failure likelihood, the method uses a reliability model. The method constitutes theoretical foundation for the cost-effective development of software that attempts to reduce the risk of failure. Managers can use the failure-risk estimates to better determine how much testing effort can be economically justified