Who is Afraid of Figure of Speech?

Aristotle's illustrations of the fallacy of Figure of Speech (or Form of Expression) are none too convincing. They are tied to Aristotle's theory of categories and to peculiarities of Greek grammar that fail to hold appeal for a contemporary readership. Yet, upon closer inspection, Figure of Speech shows many points of contact with views and problems that inhabit 20th-century analytical philosophy. In the paper, some Aristotelian examples will be analyzed to gain a better understanding of this fallacy. The case of the Third Man argument and some modern cases lend plausibility to the claim that Figure of Speech is of more interest as a type of fallacy than has generally been assumed. Finally, a case is made for the view that Figure of Speech, though listed among the fallacies dependent upon language, is not properly classified as a fallacy of ambiguity. More likely, it should be looked upon as a type of non sequitur. This has important consequences for the profile of dialogue associated with this fallacy.     

Effects of diagnostic classification systems on clinical hypothesis-generation

In this study it was determined whether (a) classification as opposed to absence of classification has an effect on the quality of clinical hypotheses (b) the DSM-III-R and the CBCL have a different effect on the quality of clinical hypotheses, and (c) the potential difference between the DSM-III-R and the CBCL is moderated by the different number of syndromes identified by these systems. To investigate these questions, an experiment was conducted in which 86 clinicians generated hypotheses for six cases. The clinicians were divided into a DSM-III-R, a CBCL, and a control group. Of the six cases, two were classified by both classification systems as one syndrome, two were classified as one syndrome by the DSM-III-R but as two syndromes by the CBCL, and two were classified as two syndromes by the DSM-III-R but as one syndrome by the CBCL. The quality of the hypotheses was determined by means of four dependent variables selected from an overview of qualitative criteria: explanatory value, redundancy, possibility of operationalization, and specificity. No differences between the CBCL and the control groups were found. The DSM group performed better than the control group regarding explanatory value and redundancy. The DSM-III-R group also scored better than the CBCL group regarding explanatory value, particularly when the number of identified syndromes was two for the CBCL and one for the DSM-III-R.     

Two simple classes of mastery scores based on the beta-binomial model

Two simple classes of mastery scores which are suitable for hand calculations are presented for beta-binomial test score distributions combined with linear and cubic referral success. The models provide a simple way to explore the consequences of selecting an arbitrary mastery score. Such assessment would be useful whenever the test user is not willing to posta priori a loss ratio, but wishes to look at the various consequences before aiming at a particular score.     

Diagnostic Classification Models for a Mixture of Ordered and Non-ordered Response Options in Rating Scales

When developing ordinal rating scales, we may include potentially unordered response options such as “Neither Agree nor Disagree,” “Neutral,” “Don’t Know,” “No Opinion,” or “Hard to Say.” To handle responses to a mixture of ordered and unordered options, Huggins-Manley et al. (2018) proposed a class of semi-ordered models under the unidimensional item response theory framework. This study extends the concept of semi-ordered models into the area of diagnostic classification models. Specifically, we propose a flexible framework of semi-ordered DCMs that accommodates most earlier DCMs and allows for analyzing the relationship between those potentially unordered responses and the measured traits. Results from an operational study and two simulation studies show that the proposed framework can incorporate both ordered and non-ordered responses into the estimation of the latent traits and thus provide useful information about both the items and the respondents.     

Merit,fit, and basic desert

Basic desert is central to the dispute between compatibilists and incompatibilists over the four-case manipulation argument. I argue that there are two distinct ways of understanding the desert salient to moral responsibility; moral desert can be understood as a claim about fitting responses to an agent or as a claim about the merit of the agent. Failing to recognize this distinction has contributed to a stalemate between both sides. I suggest that recognizing these distinct approaches to moral desert will help clarify a central source of disagreement between compatibilists and incompatibilists and assist both sides in resolving the current stalemate.     

Co-learning binary classifiers for LP-based multi-label classification

A simple yet practical multi-label learning method, called label powerset (LP), considers each different combination of labels that appear in the training set as a different class value of a single-label classification task. However, because those classes source from multiple labels, there may be some inherent relationships among them. To tackle this challenge, we propose a novel model which aims to co-learn binary classifiers, by combining the training of binary classifiers and the characterizing the relationship among them into a unified objective function. In addition, we develop an alternating optimization algorithm to solve the proposed problem. Extensive experimental results on various kinds of datasets well demonstrate the effectiveness of the proposed model.     

Stopping rules for multi-category computerized classification testing

Computerized classification testing (CCT) aims to classify persons into one of two or more possible categories to make decisions such as mastery/non-mastery or meet most/meet all/exceed. A defining feature of CCT is its stopping criterion: the test terminates when there is enough confidence to make a decision. There is abundant research on CCT with a single cut-off, and two common stopping criteria are the sequential probability ratio test (SPRT) statistic and the generalized likelihood ratio statistic (GLR). However, there is a relative scarcity of research extending the SPRT to the multi-hypothesis case for when there is more than one cut-off. In this paper, we propose a new multi-category GLR (mGLR) statistic as well as a stochastically curtailed version of the CCT with three or more categories. A simulation study was conducted to show that the mGLR statistic outperformed the existing stopping rules by generating shorter average test length without sacrificing classification accuracy. Results also revealed that the stochastically curtailed mGLR successfully increased test efficiency in certain testing conditions.