ECONOMIC RATIONALISM: AN IDEOLOGY OF EXCLUSION

Policies to rely on market forces instead of government responsibility were effected by techniques of exclusion. Political leaders, senior Federal bureaucrats and the managers of State bureaucracies advocated one version of economics. Critics were ignored or removed. The welfare state was derided and a two tier society encouraged. Only a re-vitalized politics and economics of social justice can challenge the economic rationalists' promotion of individualism, greed and intolerance.     

Supervising Mental Health Clinicians in The Context of Complex Organisational Dynamics

This article discusses the complex dynamics and challenges of providing supervision to mental health clinicians working within an organisational context when the supervisor is not part of the organisation and has no formal managerial or accountability responsibility. It considers two models of supervision and their application to three supervisory relationships in an organisational context. It concludes that for supervision to be effective there needs to be a consideration of the social defences in operation within the organisation and how they are met by the anxieties and defences of the supervisor and supervisee. This requires a mixed approach by the supervisor of the technical with the inter-subjective as a reliance on one model of supervision risks limiting the effectiveness of the intervention. The role of the supervisor can enable the supervisee to establish a clinical framework and the necessary environment essentials for their practice.     

ORGANIZATIONAL ENVIRONMENT,DIFFERENTIATION AND PERCEIVED ENVIRONMENTAL UNCERTAINTY1

Four propositions are derived from an a priori model which relates environmental characteristics, organizational differentiation, and perceived environmental uncertainty. The four propositions are: (P₁) the greater the magnitude of each of several task environmental dimensions confronting an organization, the greater the degree of organizational differentiation, (P₂) the greater the degree of task environment change, the greater the degree of organizational differentiation, (P₃) the greater the size of the organization, the greater the organizational differentiation, and (P₄) the greater the organizational differentiation, the less the degree of perceived environmental uncertainty seen by top administrators. These propositions were tested on 23 Employment Service District Offices with objective environmental and organizational differentiation measures and with self report indices of perceived environmental uncertainty. Zero-order correlational and regression analyses supported propositions P₁, P₃, and P₄. Based on the empirical findings, a revised model of environment, differentiation and uncertainty was developed. Suggestions for future research and theorizing are offered.     

AN ADAPTIVE MULTIVARIATE APPROACH TO TIME SERIES FORECASTING

In recent years, time series analysts have shifted their interest from univariate to multivariate forecasting approaches. Among them, the Box-Jenkins transfer function process and the state space method have received the most attention. This paper presents a simplified approach that embodies some desirable features of existing methods. It stresses empirical analysis, has a unified modeling structure, is easily applicable, and is adaptive to changes without necessitating prior information on the evolution of a system under study. The core of the method relies on the Carbone-Longini adaptive estimation procedure (AEP). Results of a comparative study based on the well-known Lydia E. Pinkham data and the Box-Jenkins sales/leading indicator data illustrate the merits of multivariate AEP in improving forecasting accuracy while simplifying the analysis process. Subject Area: Forecasting.     

AN ADAPTIVE APPROACH TO TIME-SERIES FORECASTING

This paper extends the applicability of the Carbone-Longini adaptive estimation procedure (AEP) to time-series forecasting. Comparisons with adaptive filtering, the Box-Jenkins methodology, and multiple regression analysis as it applies to time-series analysis are provided. Specific time-series data examined by Box and Jenkins and Box and Tiao constitute the basis for these comparisons. The analysis of the results indicate the robustness and performance superiority of the simple distributive-lag forecast model coupled with the concept of adaptively “tracking” rather than “fitting” historical data.