Contextualized Kinds

If identification of contextualized kinds is all that we possibly get, we have to deny property monism (non-contextualized high-level kinds are not physical) or, as is sometimes argued, we have to accept that predicates and terms allegedly picking out high-level kinds do not pick out kinds at all (this is Kim s strategy (e.g. Kim 2002)) - again an idea that can be nicely modeled within the present framework. On this latter view, we do not get identification because the property structures do not determine any entity at all. No property corresponds to the property structure -hence, we do not get identification. All we get is the property structure itself. Not so for the contextualized descriptions. When presented under a contextualized description, a kind x reduced to x when presented under a description that is more transparent to the properties in virtue of which it behaves the way it does, or is what it is. Directionality and diversity, as cashed out above, are, thus, constitutive for the contextualized kind approach. 

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As already pointed out in the introduction, unity may come in degrees. There are weaker forms of unity that build upon notions weaker than that of identity. These have played a prominent role in the reduction debate in the philosophy of mind and should, thus, be mentioned briefly. Supervenience (Kim 1982, 1984, 1985, 1987) could be a serious candidate. Any contextualized-kind approach to reductionism could be extended to a general reductionist claim that connects some realization base with the relevant supervenient high-level kinds. On some occasions, Kim discusses reduction via supervenience (1984). It should be noted, however, that according to any notion of supervenience, in a straightforward sense, what supervenes or is realized is not (necessarily) identical to its supervenience-, or realization-base. 

The basic Nordic School approach, that of assessing satisfaction with a cross-section survey or appraisal raises one important dilemma. A criticism of the approach is that, when analysts and stakeholders obtain a set of scores, they may have difficulty in understanding their meaning and lack a context of interpretation. This issue is at the core of the charge made by Parasuraman and colleagues that the expectations approach is superior because it offers valuable contextual information (Parasuraman et al, 1985, 1994b). There are, however, three kinds of extensions to the basic Nordic School system that can augment the value of the post hoc only appraisals. 

The contextuality of meaning may be called a principle , for it is neither a brute fact nor a law of nature. But exactly what is meant by a principle is hard to specify. We can give some familiar examples of course. In ecology there is the well-known competitive exclusion principle , which explains why organisms occupying exactly the same niche cannot coexist for more than a brief period of time. In logic we all use, whether we know it or not, the principle of contradiction , which states that two propositions that really contradict each other cannot both be true. And since, by implication, at least one of them must be false, we justify the kind of hypothetico-deductive scientific... 

The underlying premise here is that language can create a sense of activity and intensity of the kind that one usually associates exclusively with visual action. Dialogue and visuals must be partners in the short script our goal in this chapter is to show you how to make them partners. Before we do so, however, we will begin with a warning don t use too much dialogue. This may seem to contradict the previous assertion, but a few illustrations will clarify and contextualize the caution. 

During the mathematical specification of the simulation model there is no need to bother about the correct parameter values to be used during the Monte Carlo simulation. Of course, this is addressed prior to running the simulations. In principle there are three kinds of sources for parameter values. The ideal source would consist of sufficient statistical data that has been gathered under the various contextual conditions for which the risk assessment has to be performed. In practice such ideal sources almost never exist. Instead one typically has to work with limited statistical data that has been gathered under different conditions. Fortunately there often are two complementary sources domain expertise and scientific expertise (on safety and human factors). In the context of Monte Carlo... 

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