Input-output evaluation of rural development: a forestry-centred application

The purpose of this paper is to analyse the role of forestry in the rural economy of Scotland. The Generation of Regional Input-Output Tables (GRIT) technique is applied to the estimation of input-output tables for rural Scotland. This is followed by a forestry-centred multiplier analysis. The results suggest that the forestry planting and harvesting sectors offer great potential for improving economic activity. However, their backward linkages with other rural industries seem to be limited. On the other hand, the backward linkages of wood-processing industries with the rest of the economy are strong. From this analysis, it is concluded that the joint development of forestry and wood-processing industries would promote economic development in rural Scotland.     

A LOOK AT THE SEMANTIC DIFFERENTIAL AS A TOOL TO ASSIST FACULTY TEACHING EVALUATIONS

The evaluation of the effectiveness of faculty teaching is a difficult process. This paper presents the results of an experimental empirical investigation into the possibility of using a psycho-linguistic measurement technique, the Semantic Differential, to measure faculty communication of terminology to students. This measurement could be used to supplement other teaching evaluation devices. The paper determines a set of “key concepts,” derives a set of S.D. meanings from a Faculty to act as a standard, and measures student meanings at the beginning and end of an introductory course. These sets of meanings are then compared.     

A HEURISTIC ALGORITHM FOR RESOURCE LEVELING IN MULTI-PROJECT,MULTI-RESOURCE SCHEDULING

The utility of conventional project management techniques, such as PERT and CPM, is hampered by significant and realistic network attributes and managerial considerations. There are many situations which involve more than one project and numerous resources. Of major concern in such situations is the reduction of the maximum quantity of each required resource. The algorithm presented here is capable of scheduling the individual activities in multiple projects with multiple resources and leveling the overall quantity of each resource which is required. The basis of the algorithm is a sequencing procedure for examining various combinations of activity start times. The measure of effectiveness for each combination is the sum of the squares of the required quantity of each resource in each time period.     

Open Tertiary Education in Australia

The draft report of the Committee on Open University, Open Tertiary Education, is criticized on the grounds that it is overly conservative and makes little contribution to the opening up of tertiary education in Australia. The problems inherent in government committees, particularly in the field of education, are considered. Using the data provided in the report, the current closed system of tertiary education is criticized and a number of proposals, alternative to those made in the report, are presented for consideration.     

A hierarchical model for ordinal matrix factorization

This paper proposes a hierarchical probabilistic model for ordinal matrix factorization. Unlike previous approaches, we model the ordinal nature of the data and take a principled approach to incorporating priors for the hidden variables. Two algorithms are presented for inference, one based on Gibbs sampling and one based on variational Bayes. Importantly, these algorithms may be implemented in the factorization of very large matrices with missing entries.     

The Risk of James–Stein and Lasso Shrinkage

This article compares the mean-squared error (or ?₂ risk) of ordinary least squares (OLS), James–Stein, and least absolute shrinkage and selection operator (Lasso) shrinkage estimators in simple linear regression where the number of regressors is smaller than the sample size. We compare and contrast the known risk bounds for these estimators, which shows that neither James–Stein nor Lasso uniformly dominates the other. We investigate the finite sample risk using a simple simulation experiment. We find that the risk of Lasso estimation is particularly sensitive to coefficient parameterization, and for a significant portion of the parameter space Lasso has higher mean-squared error than OLS. This investigation suggests that there are potential pitfalls arising with Lasso estimation, and simulation studies need to be more attentive to careful exploration of the parameter space.