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In this essay, I show how Thomas Aquinas circumscribes epistemological questions concerning both the possibility and character of our knowledge of God within a larger eschatological framework that acknowledges the beatific vision as the ultimate good that we desire as well as the ultimate end for which we were created. Thus, knowledge of God is possible and actual on Aquinas's view because it is eternally rather than merely temporally indexed—that is, properly attributable to the blessed in heaven and only derivatively attributable to persons of faith. I further argue that interpreting Aquinas's account of faith in the light of his account of the beatific vision allows us to carve out polemical space for the theologically realist claim that there can be and in fact is objectivity in our knowledge of God, whether that knowledge comes through faith (in this life) or the beatific vision (in the next life). 相似文献
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Dungang Liu Xiaorui Zhu Brandon Greenwell Zewei Lin 《The British journal of mathematical and statistical psychology》2023,76(1):192-210
Probit models are used extensively for inferential purposes in the social sciences as discrete data are prevalent in a vast body of social studies. Among many accompanying model inference problems, a critical question remains unsettled: how to develop a goodness-of-fit measure that resembles the ordinary least square (OLS) R2 used for linear models. Such a measure has long been sought to achieve ‘comparability’ of different empirical models across multiple samples addressing similar social questions. To this end, we propose a novel R2 measure for probit models using the notion of surrogacy – simulating a continuous variable as a surrogate of the original discrete response (Liu & Zhang, Journal of the American Statistical Association, 113, 845 and 2018). The proposed R2 is the proportion of the variance of the surrogate response explained by explanatory variables through a linear model, and we call it a surrogate R2. This paper shows both theoretically and numerically that the surrogate R2 approximates the OLS R2 based on the latent continuous variable, preserves the interpretation of explained variation, and maintains monotonicity between nested models. As no other pseudo R2, McKelvey and Zavoina's and McFadden's included, can meet all the three criteria simultaneously, our measure fills this crucial void in probit model inference. 相似文献
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