Discriminative Assumptions of Utilitarian Bioethics Regarding Individuals with Intellectual Disabilities

'Bioutilitarianism', which is a position presented by the well-known utilitarian philosophers Helga Kuhse, James Rachels and Peter Singer, views the potential existence of infants with intellectual disabilities as less valuable than the existence of 'normal' infants. I will argue that their arguments are based on misleading and biased information about the characteristics of individuals with intellectual disabilities and their quality of life. By ignoring the social constructionist dimension of disability the bioutilitarians make questionable moral conclusions. Also, the underlying assumption of their position is an 'intelligist' intuition, namely that intellectually 'normal' human beings are morally more valuable than human beings with intellectual disabilities. I contend that there is no reasonable basis for viewing infants with intellectual disabilities as being less entitled to life than 'normal' infants or for considering people with intellectual disabilities as less valuable than 'normal' people.     

Predicting online gambling self-exclusion: an analysis of the performance of supervised machine learning models

As gambling operators become increasingly sophisticated in their analysis of individual gambling behaviour, this study evaluates the potential for using machine learning techniques to identify individuals who used self-exclusion tools out of a sample of 845 online gamblers, based on analysing trends in their gambling behaviour. Being able to identify other gamblers whose behaviour is similar to those who decided to use self-exclusion tools could, for instance, be used to share responsible gaming messages or other information that aids self-aware gambling and reduces the risk of adverse outcomes. However, operators need to understand how accurate models can be and which techniques work well. The purpose of the article is to identify the most accurate technique out of four highly diverse techniques and to discuss how to deal analytically and practically with a rare event like self-exclusion, which was used by fewer than 1% of gamblers in our data-set. We conclude that balanced training data-sets are necessary for creating effective models and that, on our data-set, the most effective method is the random forest technique which achieves an accuracy improvement of 35 percentage points versus baseline estimates.     

Some matrix results related to a partitioned singular linear model

Consider the linear model (y, Xβ V), where the model matrix X may not have a full column rank and V might be singular. In this paper we introduce a formula for the difference between the BLUES of Xβ under the full model and the model where one observation has been deleted. We also consider the partitioned linear regression model where the model matrix is (X₁: X₂) the corresponding vector of unknown parameters being (β′₁ : β′₂)′. We show that the BLUE of X₁ β₁ under a specific reduced model equals the corresponding BLUE under the original full model and consider some interesting consequences of this result.     

The Equality of the Ordinary Least Squares Estimator and the Best Linear Unbiased Estimator

It is well known that the ordinary least squares estimator of Xβ in the general linear model E _y = Xβ, cov y = σ² V, can be the best linear unbiased estimator even if V is not a multiple of the identity matrix. This article presents, in a historical perspective, the development of the several conditions for the ordinary least squares estimator to be best linear unbiased. Various characterizations of these conditions, using generalized inverses and orthogonal projectors, along with several examples, are also given. In addition, a complete set of references is provided.     

Effect of adding regressors on the equality of the BLUEs under two linear models

In this paper we consider the estimation of regression coefficients in two partitioned linear models, shortly denoted as , and , which differ only in their covariance matrices. We call and full models, and correspondingly, and small models. We give a necessary and sufficient condition for the equality between the best linear unbiased estimators (BLUEs) of X₁β₁ under and . In particular, we consider the equality of the BLUEs under the full models assuming that they are equal under the small models.     

Exploring normativity in disability studies

Normativity is a concept that is often misapplied in disability studies, especially in ‘postconventional’ accounts, where the concept is conflated with ‘normal’, ‘normate’, or ‘standard’. This article addresses this confusion, explores the meaning and use of ‘normativity’, and presents some analytic tools to discuss normative issues of right and wrong. The article finishes by discussing examples where conceptual confusions result in confused normative judgments focusing in particular on agency, responsibility and moral status. The article argues that disability research should carefully consider the use of theories and empirical knowledge in the light of their ethical implications as well as the lived experiences of disability.