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This paper explores the integration of Open Educational Practices (OEP) into an institutional strategy to develop distinctive excellence in teaching, learning and scholarship. The institution in the case study is a public polytechnic university serving a metropolitan area in Canada. If emerging Open Educational Practices are to flourish at our university, support for OEP must integrate with and contribute to our broader efforts to clarify and enhance our strategic position.We have identified three focal points where our institution can focus attention in order to ensure that our use of emerging Open Educational Practices will best align with, contribute to, and benefit from our institutional strategy for distinctive excellence in teaching and learning:• Opening up the pedagogy underlying exemplary OER, to enable a deeper faculty engagement in integrating and mobilizing diverse sources of knowledge in teaching; • Opening up that process by which individual faculty improve teaching and learning, as a model for our students' own engagements with knowledge; • Opening up our collective faculty work in innovation networks, as a model for students and as a signature institutional strength and outcome.We summarize the rationale and planned next steps for each of these focal points, which are intended to cumulatively build on each other as a value chain to support the development of distinctive graduate capabilities as signature outcomes of our teaching and learning.

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Networks distribute energy, materials and information to the components of a variety of natural and human-engineered systems, including organisms, brains, the Internet and microprocessors. Distribution networks enable the integrated and coordinated functioning of these systems, and they also constrain their design. The similar hierarchical branching networks observed in organisms and microprocessors are striking, given that the structure of organisms has evolved via natural selection, while microprocessors are designed by engineers. Metabolic scaling theory (MST) shows that the rate at which networks deliver energy to an organism is proportional to its mass raised to the 3/4 power. We show that computational systems are also characterized by nonlinear network scaling and use MST principles to characterize how information networks scale, focusing on how MST predicts properties of clock distribution networks in microprocessors. The MST equations are modified to account for variation in the size and density of transistors and terminal wires in microprocessors. Based on the scaling of the clock distribution network, we predict a set of trade-offs and performance properties that scale with chip size and the number of transistors. However, there are systematic deviations between power requirements on microprocessors and predictions derived directly from MST. These deviations are addressed by augmenting the model to account for decentralized flow in some microprocessor networks (e.g. in logic networks). More generally, we hypothesize a set of constraints between the size, power and performance of networked information systems including transistors on chips, hosts on the Internet and neurons in the brain.