Humanising Managerialism: Reclaiming Emotional Reasoning,Intuition, the Relationship,and Knowledge and Skills in Social Work

This paper develops Professor Eileen Munro's coverage of ‘emotional dimensions’ in her Review of Child Protection in England. It argues that managerialism has failed to recognise the importance of the emotional life of human beings and the importance of the relationships we build in social work and that this failure seriously hinders the quality and effectiveness of social work. The paper begins with an account of what an ‘emotional dimension’ might encompass and, drawing on conceptualisations mainly from neuroscience, looks at what is meant by the words emotions and feelings, affect, attunement and empathy. A second section looks at the skewed representation of logical thinking as innately superior to emotional and intuitive reasoning and the part played by conscious and unconscious elements within judicial decision-making. It then analyses the dangers evident in the more extreme and rigid forms of managerialism that can be found in some areas of social work and a final section argues that for managerialism to be humanised it calls for an emotionally responsive relationship-based practice to be located at the heart of social work.     

On the foundations: Statistical models and inference

This paper presents an overview of some recent results concerning statistical models and inference. specifically: grounds for statistical models. types of models that simplify by standard probability analysis, the use of categorical information in the reduction of the model with data, and the role of additives in the inference process. The relevant technical material has been developed elsewhere.     

A Longitudinal Study of the Leadership Development Process: Individual Differences Predicting Leader Effectiveness

This study tracked the leadership development of236 male cadets from matriculation through graduation ata military college. Cognitive ability, physical fitness,prior influence experiences, and self-esteem measured in Year 1 were relevant to predictingthose who assumed formal leadership positions in Year 4.Physical fitness and prior influence experiencesmeasured when cadets entered the college predicted leader effectiveness rated in their fourthyear. Stress tolerance and moral reasoning levels didnot predict leader emergence or effectiveness, thoughthe set of individual difference measures significantly predicted emergence and effectiveness. Physicalfitness levels and moral reasoning increased over timefor all cadets, though surprisingly, levels ofself-esteem and stress tolerance did not increase over time. Overall the study demonstrated thatleadership effectiveness and emergence could bepredicted from early measures of individualdifferences.     

Multi-group segregation for nominal and ordinal categorical data: An application to socio-religious groups in India

This study is an attempt to construct some summary measures of multi-group segregation for nominal and ordinal categorical data. These measures are developed by taking into account the association between identity groups and unordered or ordered categories of the well-being indicator and also the disproportionate representation of the populations of the identity groups across the unordered or ordered categories of the well-being indicator. The newly developed measures are characterized and applied to assess the disparities in education and occupational status among the socio-religious groups in India. Empirical findings reveal the existence of between-group inequality in education and occupation in India, and some relevant policies to reduce these inequalities are also suggested.     

The Role of Intuition in Risk/Benefit Decision-Making in Human Subjects Research

One of the key principles of ethical research involving human subjects is that the risks of research to should be acceptable in relation to expected benefits. Institutional review board (IRB) members often rely on intuition to make risk/benefit decisions concerning proposed human studies. Some have objected to using intuition to make these decisions because intuition is unreliable and biased and lacks transparency. In this article, I examine the role of intuition in IRB risk/benefit decision-making and argue that there are practical and philosophical limits to our ability to reduce our reliance on intuition in this process. The fact that IRB risk/benefit decision-making involves intuition need not imply that it is hopelessly subjective or biased, however, since there are strategies that IRBs can employ to improve their decisions, such as using empirical data to estimate the probability of potential harms and benefits, developing classification systems to guide the evaluation of harms and benefits, and engaging in moral reasoning concerning the acceptability of risks.     

Estimation of nonlinear CUB models via numerical optimization and EM algorithm

The analysis of human perceptions is often carried out by resorting to surveys and questionnaires, where respondents are asked to express ratings about the objects being evaluated. A class of mixture models, called CUB (Combination of Uniform and shifted Binomial), has been recently proposed in this context. This article focuses on a model of this class, the Nonlinear CUB, and investigates some computational issues concerning parameter estimation, which is performed by Maximum Likelihood. More specifically, we consider two main approaches to optimize the log-likelihood: the classical numerical methods of optimization and the EM algorithm. The classical numerical methods comprise the widely used algorithms Nelder–Mead, Newton–Raphson, Broyden–Fletcher–Goldfarb–Shanno (BFGS), Berndt–Hall–Hall–Hausman (BHHH), Simulated Annealing, Conjugate Gradients and usually have the advantage of a fast convergence. On the other hand, the EM algorithm deserves consideration for some optimality properties in the case of mixture models, but it is slower. This article has a twofold aim: first we show how to obtain explicit formulas for the implementation of the EM algorithm in nonlinear CUB models and we formally derive the asymptotic variance–covariance matrix of the Maximum Likelihood estimator; second, we discuss and compare the performance of the two above mentioned approaches to the log-likelihood maximization.     

法律修辞与逻辑

论者通常把修辞与逻辑对立起来,很少论及二者如何统一于论证过程,而在法律领域尤其需要这种统一.法律的学科特点在于其推理前提的开放性和结论的封闭性,因而在法律推理过程中,需要通过修辞将开放的前提集合论证为可接受的封闭性前提集合,也需要通过逻辑根据封闭的前提得出必然的结论.文章通过对法律推理过程及其特点的分析,论证了法律修辞与逻辑的统一性,即逻辑是修辞,是最具说服力的一种修辞;修辞也是逻辑,是在无法直接进行演绎推理时所备选的逻辑.其目的都在于提高法律推理结论的可接受性.     

从历史形态走向理论形态——兼评三卷本《中国古代文学理论体系》

把古典形态的历史上的文学批评整理为现代形态的文学批评的历史,是2 0世纪批评史研究界的主要贡献所在,文章追溯了2 0世纪批评史研究范式从侧重历史研究向侧重理论研究转变的进程,并以三卷本《中国古代文学理论体系》为例证,揭示了理论研究的特点和未来可能的研究方向。      

Efficient estimators with categorical ranked set samples: estimation procedures for osteoporosis

Ranked set sampling (RSS) design as a cost-effective sampling is a powerful tool in situations where measuring the variable of interest is costly and time-consuming; however, ranking information about sampling units can be obtained easily through inexpensive and easy to measure characteristics at little or no cost. In this paper, we study RSS data for analysis of an ordinal population. First, we compare the problem of non-representative extreme samples under RSS and commonly-used simple random sampling. Using RSS data with tie information, we propose non-parametric and maximum likelihood estimators for population parameters. Through extensive numerical studies, we investigate the effect of various factors including ranking ability, tie generating mechanisms, the number of categories and population setting on the performance of the estimators. Finally, we apply the proposed methods to the bone disorder data to estimate the proportions of patients with osteopenia and osteoporosis status.     

A model for comparisons with ordered categorical data

A model developed by Andrich for ordered categorical data is extended to develop tests for treatment effects with paired or matched samples. In particular, this includes analysis for pre-post studies and crossover designs. Some advantages of this model are that it allows for misclassification of subjects, yields reasonable conditional requirements for exact analysis, a normal approximation is good for all but the smallest of sample sizes, and it is relatively simple mathematically. Furthermore, the form of the tests derived are logical extensions of tests for unordered categories.