Two competing accounts of value incomparability have been put forward in the recent literature. According to the standard account, developed most famously by Joseph Raz, 'incomparability' means determinate failure of the three classic value relations (better than, worse than, and equally good): two value-bearers are incomparable with respect to a value V if and only if (i) it is false that x is better than y with respect to V, (ii) it is false that x is worse than y with respect to V and (iii) it is false that x and y are equally good with respect to V. Most philosophers have followed Raz in adopting this account of incomparability. Recently, however, John Broome has advocated an alternative view, on which value incomparability is explained in terms of vagueness or indeterminacy. In this paper I aim to further Broome's view in two ways. Firstly, I want to supply independent reasons for thinking that the phenomenon of value incomparability is indeed a matter of the indeterminacy inherent in our comparative predicates. Secondly, I attempt to defend Broome's account by warding off several objections that worry him, due mainly to Erik Carlson and Ruth Chang.
Some comparisons are hard. How should we think about such comparisons? According to John Broome, we should think about them in terms of vagueness. But the vagueness account has remained unpopular thus far. Here I try to bolster it by clarifying the notion of comparative vagueness that lies at its heart.
The study aimed to development a prediction model for soil erosion degree by image analysis techniques. The spectral information was obtained by image analysis in the RGB and HSB color system, and by calculus resulted rgb normalized values. Specific indices were calculated: intensity (INT), normalized difference index (NDI) and dark green color index (DGCI). The correlation analysis emphasized the existence of high levels of interdependence between specific indices and normalized color data rgb, respectively luminance (L). The regression analysis has enabled the creation of estimation models for soil erosion degree (DSE), in the form of linear equations in relation to luminance (R2=0.999, p<<0.001, RMSEP=25.5766) and INT (R2=0.998, p<<0.001, RMSEP=25.5833), and 2nd degree polynomial equations in relation to DGCI (R2=0.768, p<0.001, RMSEP=28.3275). Clustering analysis facilitated the grouping of the studied cases in two distinct clusters with four sub-clusters, under conditions of statistical accuracy, Coph. corr. = 0.831.
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