In this paper, I argue that medical school admissions should be limited to statistically relevant factors. My argument rests primarily on three assumptions. A state professional school should maximize production. If a state professional school should maximize production, then it should maximize production per student. If a state professional school should maximize production per student, then, within the optimum budget, a state medical school should maximize quality-adjusted medical services per graduate. I put forth a tentative equation for ranking applicants as (...) a way of maximizing quality-adjusted medical services per graduate. This way of ranking is cheaper than the way admissions is currently done. Hence, the proposal is practically as well as theoretically appealing. (shrink)

The term ‘intelligence’ as used in this paper refers to items of knowledge collected for the sake of assessing and maintaining national security. The intelligence community (IC) of the United States (US) is a community of organizations that collaborate in collecting and processing intelligence for the US. The IC relies on human-machine-based analytic strategies that 1) access and integrate vast amounts of information from disparate sources, 2) continuously process this information, so that, 3) a maximally comprehensive understanding of world actors (...) and their behaviors can be developed and updated. Herein we describe an approach to utilizing outcomes-based learning (OBL) to support these efforts that is based on an ontology of the cognitive processes performed by intelligence analysts. Of particular importance to the Cognitive Process Ontology is the class Representation that is Warranted. Such a representation is descriptive in nature and deserving of trust in its veridicality. The latter is because a Representation that is Warranted is always produced by a process that was vetted (or successfully designed) to reliably produce veridical representations. As such, Representations that are Warranted are what in other contexts we might refer to as ‘items of knowledge’. (shrink)